@inproceedings{Simpson2019PredictingHA,
  title={Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning},
  author={Edwin Simpson and Erik-L{\^a}n Do Dinh and Tristan Miller and Iryna Gurevych},
  year={2019}
}
@article{Simpson2018FindingCA,
  title={Finding Convincing Arguments Using Scalable Bayesian Preference Learning},
  author={Edwin Simpson and Iryna Gurevych},
  journal={Transactions of the Association for Computational Linguistics},
  year={2018},
  volume={6},
  pages={357-371}
}

@inproceedings{field2019contextual,
  title={Contextual affective analysis: A case study of people portrayals in online\# metoo stories},
  author={Field, Anjalie and Bhat, Gayatri and Tsvetkov, Yulia},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={13},
  number={01},
  pages={158--169},
  year={2019}
}

@inproceedings{An2018SemAxisAL,
  title={SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment},
  author={Jisun An and Haewoon Kwak and Yong-Yeol Ahn},
  booktitle={ACL},
  year={2018}
}

@article{joseph_relating_2016,
  title = {Relating Semantic Similarity and Semantic Association to How Humans Label Other People},
  author = {Joseph, Kenneth and Carley, Kathleen M.},
  year = {2016},
  pages = {1},
  journal = {NLP+ CSS 2016}
}




@misc{OntoNotes,
  title = {{OntoNotes} V5 release},
  howpublished = {\url{https://catalog.ldc.upenn.edu/LDC2013T19}},
  note = {Accessed: 2019-08-22}
}

@inproceedings{Hasan2017WordRV,
  title={Word Re-Embedding via Manifold Dimensionality Retention},
  author={Souleiman Hasan and Edward Curry},
  booktitle={EMNLP},
  year={2017}
}

@article{Roweis2000NonlinearDR,
  title={Nonlinear dimensionality reduction by locally linear embedding.},
  author={Sam T. Roweis and Lawrence K. Saul},
  journal={Science},
  year={2000},
  volume={290 5500},
  pages={
          2323-6
        }
}

@book{heise_expressive_2007-1,
  title = {Expressive {{Order}}},
  publisher = {{Springer}},
  author = {Heise, David R.},
  year = {2007}
}

@article{beukeboom_mechanisms_2014,
  title = {Mechanisms of Linguistic Bias: {{How}} Words Reflect and Maintain Stereotypic Expectancies},
  volume = {31},
  shorttitle = {Mechanisms of Linguistic Bias},
  journal = {Social cognition and communication},
  author = {Beukeboom, Camiel J. and others},
  year = {2014},
  pages = {313--330}
}

@book{heise_understanding_1979-1,
  title = {Understanding Events: {{Affect}} and the Construction of Social Action},
  shorttitle = {Understanding Events},
  publisher = {{CUP Archive}},
  author = {Heise, David R.},
  year = {1979}
}

@article{bamman_gender_2014,
  title = {Gender Identity and Lexical Variation in Social Media},
  volume = {18},
  number = {2},
  journal = {Journal of Sociolinguistics},
  author = {Bamman, David and Eisenstein, Jacob and Schnoebelen, Tyler},
  year = {2014},
  pages = {135--160}
}

@article{mirzaev_considerations_2019,
  title={Considerations for the Interpretation of Bias Measures of Word Embeddings},
  author={Mirzaev, Inom and Schulte, Anthony and Conover, Michael and Shah, Sam},
  journal={arXiv preprint arXiv:1906.08379},
  year={2019}
}

@article{resnik_semantic_1999,
  title = {Semantic Similarity in a Taxonomy: {{An}} Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language},
  volume = {11},
  shorttitle = {Semantic Similarity in a Taxonomy},
  journal = {J. Artif. Intell. Res.(JAIR)},
  author = {Resnik, Philip},
  year = {1999},
  pages = {95-130}
}
@inproceedings{mikolov2018advances,
  title={Advances in Pre-Training Distributed Word Representations},
  author={Mikolov, Tomas and Grave, Edouard and Bojanowski, Piotr and Puhrsch, Christian and Joulin, Armand},
  booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
  year={2018}
}

@article{firth_synopsis_1957,
  title = {A Synopsis of Linguistic Theory, 1930-1955},
  journal = {Studies in linguistic analysis},
  author = {Firth, John R.},
  year = {1957}
}

@inproceedings{speer2017conceptnet,
    title = {{ConceptNet} 5.5: An Open Multilingual Graph of General Knowledge},
    url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972},
    author = {Speer, Robyn and Chin, Joshua and Havasi, Catherine},
    year = {2017},
    pages = {4444--4451}
}

@inproceedings{cotterell_explaining_2017,
  address = {{Valencia, Spain}},
  title = {Explaining and {{Generalizing Skip}}-{{Gram}} through {{Exponential Family Principal Component Analysis}}},
  abstract = {The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.},
  booktitle = {Proceedings of the 15th {{Conference}} of the {{European Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{Volume}} 2, {{Short Papers}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Cotterell, Ryan and Poliak, Adam and Van Durme, Benjamin and Eisner, Jason},
  month = apr,
  year = {2017},
  pages = {175--181}
}

@article{baer_exponential_2018,
  title = {Exponential {{Family Word Embeddings}}: {{An Iterative Approach}} for {{Learning Word Vectors}}},
  shorttitle = {Exponential {{Family Word Embeddings}}},
  author = {Baer, Benjamin and Seto, Skyler and Wells, Martin T.},
  year = {2018}
}



@article{sun_mitigating_2019,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1906.08976},
  primaryClass = {cs},
  title = {Mitigating {{Gender Bias}} in {{Natural Language Processing}}: {{Literature Review}}},
  shorttitle = {Mitigating {{Gender Bias}} in {{Natural Language Processing}}},
  abstract = {As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.},
  journal = {arXiv:1906.08976 [cs]},
  author = {Sun, Tony and Gaut, Andrew and Tang, Shirlyn and Huang, Yuxin and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Belding, Elizabeth and Chang, Kai-Wei and Wang, William Yang},
  month = jun,
  year = {2019},
  keywords = {Computer Science - Computation and Language}
}

@inproceedings{zhang_mitigating_2018,
  title = {Mitigating Unwanted Biases with Adversarial Learning},
  booktitle = {Proceedings of the 2018 {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
  publisher = {{ACM}},
  author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
  year = {2018},
  pages = {335--340}
}







@article{mitchell_prediction-based_2018,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1811.07867},
  primaryClass = {stat},
  title = {Prediction-{{Based Decisions}} and {{Fairness}}: {{A Catalogue}} of {{Choices}}, {{Assumptions}}, and {{Definitions}}},
  shorttitle = {Prediction-{{Based Decisions}} and {{Fairness}}},
  abstract = {A recent flurry of research activity has attempted to quantitatively define "fairness" for decisions based on statistical and machine learning (ML) predictions. The rapid growth of this new field has led to wildly inconsistent terminology and notation, presenting a serious challenge for cataloguing and comparing definitions. This paper attempts to bring much-needed order. First, we explicate the various choices and assumptions made---often implicitly---to justify the use of prediction-based decisions. Next, we show how such choices and assumptions can raise concerns about fairness and we present a notationally consistent catalogue of fairness definitions from the ML literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision systems.},
  journal = {arXiv:1811.07867 [stat]},
  author = {Mitchell, Shira and Potash, Eric and Barocas, Solon},
  month = nov,
  year = {2018},
  keywords = {Statistics - Applications}
}




@article{avraham_interplay_2017,
  title = {The Interplay of Semantics and Morphology in Word Embeddings},
  journal = {arXiv preprint arXiv:1704.01938},
  author = {Avraham, Oded and Goldberg, Yoav},
  year = {2017}
}


@article{antoniak_evaluating_2018-1,
  title = {Evaluating the Stability of Embedding-Based Word Similarities},
  volume = {6},
  journal = {Transactions of the Association for Computational Linguistics},
  author = {Antoniak, Maria and Mimno, David},
  year = {2018},
  pages = {107--119}
}














@article{shuster_punctuating_2017,
  title = {Punctuating Accountability: {{How}} Discursive Aggression Regulates Transgender People},
  volume = {31},
  shorttitle = {Punctuating Accountability},
  number = {4},
  journal = {Gender \& Society},
  author = {{shuster}, stef M.},
  year = {2017},
  pages = {481--502}
}


@article{blei_latent_2003,
  title = {Latent Dirichlet Allocation},
  volume = {3},
  journal = {J. Mach. Learn. Res.},
  author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
  month = mar,
  year = {2003},
  pages = {993-1022}
}




@article{turney_frequency_2010,
  title = {From Frequency to Meaning: {{Vector}} Space Models of Semantics},
  volume = {37},
  shorttitle = {From Frequency to Meaning},
  journal = {Journal of artificial intelligence research},
  author = {Turney, Peter D. and Pantel, Patrick},
  year = {2010},
  pages = {141--188}
}





@inproceedings{sweeney_transparent_2019,
  address = {{Florence, Italy}},
  title = {A {{Transparent Framework}} for {{Evaluating Unintended Demographic Bias}} in {{Word Embeddings}}},
  abstract = {Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geometric insights into unintended biases in the embedding vector space, they fail to offer an interpretable meaning for how the embeddings could cause discrimination in downstream NLP applications. In this work, we present a transparent framework and metric for evaluating discrimination across protected groups with respect to their word embedding bias. Our metric (Relative Negative Sentiment Bias, RNSB) measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. We show that our framework and metric enable useful analysis into the bias in word embeddings.},
  booktitle = {Proceedings of the 57th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Sweeney, Chris and Najafian, Maryam},
  month = jul,
  year = {2019},
  pages = {1662--1667}
}



@article{allport_nature_1954,
  title = {The Nature of Prejudice},
  author = {Allport, Gordon Willard and Clark, Kenneth and Pettigrew, Thomas},
  year = {1954}
}

@book{coates_language_2011,
  title = {Language and Gender. {{A}} Reader},
  publisher = {{John Wiley \& Sons; 2nd Edition}},
  author = {Coates, Jennifer and Pichler, Pia},
  year = {2011}
}




@article{mikolov_efficient_2013,
  title = {Efficient Estimation of Word Representations in Vector Space},
  journal = {arXiv preprint arXiv:1301.3781},
  author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
  year = {2013}
}

@article{sen_race_2016,
  title = {Race as a {{Bundle}} of {{Sticks}}: {{Designs}} That {{Estimate Effects}} of {{Seemingly Immutable Characteristics}}},
  volume = {19},
  shorttitle = {Race as a {{Bundle}} of {{Sticks}}},
  journal = {Annual Review of Political Science},
  author = {Sen, Maya and Wasow, Omar},
  year = {2016},
  pages = {499--522}
}

@article{ridgeway_gender_1999,
  title = {The Gender System and Interaction},
  volume = {25},
  number = {1},
  journal = {Annual review of sociology},
  author = {Ridgeway, Cecilia L. and {Smith-Lovin}, Lynn},
  year = {1999},
  pages = {191--216}
}



@book{bonilla-silva_racism_2006-1,
  title = {Racism without Racists: {{Color}}-Blind Racism and the Persistence of Racial Inequality in the {{United States}}},
  shorttitle = {Racism without Racists},
  publisher = {{Rowman \& Littlefield Publishers}},
  author = {{Bonilla-Silva}, Eduardo},
  year = {2006}
}



@article{kaneko_gender-preserving_2019,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1906.00742},
  primaryClass = {cs},
  title = {Gender-Preserving {{Debiasing}} for {{Pre}}-Trained {{Word Embeddings}}},
  abstract = {Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: \textbackslash{}emph\{feminine\}, \textbackslash{}emph\{masculine\}, \textbackslash{}emph\{gender-neutral\} and \textbackslash{}emph\{stereotypical\}, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.},
  journal = {arXiv:1906.00742 [cs]},
  author = {Kaneko, Masahiro and Bollegala, Danushka},
  month = jun,
  year = {2019},
  keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}
}



@misc{the_artificial_intelligence_channel_trouble_2017,
  title = {The {{Trouble}} with {{Bias}} - {{NIPS}} 2017 {{Keynote}} - {{Kate Crawford}} \#{{NIPS2017}}},
  abstract = {Kate Crawford is a leading researcher, academic and author who has spent the last decade studying the social implications of data systems, machine learning and artificial intelligence. She is a Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and a Visiting Professor at the MIT Media Lab.

December 5th, 2017},
  author = {{Kate Crawford}},
  month = dec,
  year = {2017}
}






@inproceedings{huang-etal-2018-automatic,
    title = "Automatic Dialogue Generation with Expressed Emotions",
    author = {Huang, Chenyang  and
      Za{\"\i}ane, Osmar  and
      Trabelsi, Amine  and
      Dziri, Nouha},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2008",
    doi = "10.18653/v1/N18-2008",
    pages = "49--54",
    abstract = "Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself. They learn from collections of past responses and generate one based on a given utterance without considering, speech act, desired style or emotion to be expressed. In this research, we address the problem of forcing the dialogue generation to express emotion. We present three models that either concatenate the desired emotion with the source input during the learning, or push the emotion in the decoder. The results, evaluated with an emotion tagger, are encouraging with all three models, but present better outcome and promise with our model that adds the emotion vector in the decoder.",
}

@inproceedings{rashkin-etal-2016-connotation,
    title = "Connotation Frames: A Data-Driven Investigation",
    author = "Rashkin, Hannah  and
      Singh, Sameer  and
      Choi, Yejin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1030",
    doi = "10.18653/v1/P16-1030",
    pages = "311--321",
}

@inproceedings{Rashkin2018Event2MindCI,
  title={Event2Mind: Commonsense Inference on Events, Intents, and Reactions},
  author={Hannah Rashkin and Maarten Sap and Emily Allaway and Noah A. Smith and Yejin Choi},
  booktitle={ACL},
  year={2018}
}

@inproceedings{ding-riloff-2018-human,
    title = "Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data",
    author = "Ding, Haibo  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1174",
    doi = "10.18653/v1/N18-1174",
    pages = "1919--1929",
    abstract = "We often talk about events that impact us positively or negatively. For example {``}I got a job{''} is good news, but {``}I lost my job{''} is bad news. When we discuss an event, we not only understand its affective polarity but also the reason why the event is beneficial or detrimental. For example, getting or losing a job has affective polarity primarily because it impacts us financially. Our work aims to categorize affective events based upon human need categories that often explain people{'}s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. We create classification models based on event expressions as well as models that use contexts surrounding event mentions. We also design a co-training model that learns from unlabeled data by simultaneously training event expression and event context classifiers in an iterative learning process. Our results show that co-training performs well, producing substantially better results than the individual classifiers.",
}

@inproceedings{Radford2019LanguageMA,
  title={Language Models are Unsupervised Multitask Learners},
  author={Alec Radford and Jeffrey Wu and Rewon Child and David Luan and Dario Amodei and Ilya Sutskever},
  year={2019}
}

@misc{yang2019xlnet,
    title={XLNet: Generalized Autoregressive Pretraining for Language Understanding},
    author={Zhilin Yang and Zihang Dai and Yiming Yang and Jaime Carbonell and Ruslan Salakhutdinov and Quoc V. Le},
    year={2019},
    eprint={1906.08237},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{vaswani2017attention,
    title={Attention Is All You Need},
    author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year={2017},
    eprint={1706.03762},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{devlin2018bert,
    title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
    author={Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
    year={2018},
    eprint={1810.04805},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@inproceedings{Radford2018ImprovingLU,
  title={Improving Language Understanding by Generative Pre-Training},
  author={Alec Radford},
  year={2018}
}

@misc{liu2018generating,
    title={Generating Wikipedia by Summarizing Long Sequences},
    author={Peter J. Liu and Mohammad Saleh and Etienne Pot and Ben Goodrich and Ryan Sepassi and Lukasz Kaiser and Noam Shazeer},
    year={2018},
    eprint={1801.10198},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@misc{allen2019analogies,
    title={Analogies Explained: Towards Understanding Word Embeddings},
    author={Carl Allen and Timothy Hospedales},
    year={2019},
    eprint={1901.09813},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{gultchin2019humor,
    title={Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops},
    author={Limor Gultchin and Genevieve Patterson and Nancy Baym and Nathaniel Swinger and Adam Tauman Kalai},
    year={2019},
    eprint={1902.02783},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@article{De_Arteaga_2019,
  title={Bias in bios: A case study of semantic representation bias in a high-stakes setting},
  author={De-Arteaga, Maria and Romanov, Alexey and Wallach, Hanna and Chayes, Jennifer and Borgs, Christian and Chouldechova, Alexandra and Geyik, Sahin and Kenthapadi, Krishnaram and Kalai, Adam Tauman},
  booktitle={Proceedings of the Conference on Fairness, Accountability, and Transparency},
  pages={120--128},
  year={2019}
}








@InProceedings{pmlr-v97-brunet19a,
  title =    {Understanding the Origins of Bias in Word Embeddings},
  author =   {Brunet, Marc-Etienne and Alkalay-Houlihan, Colleen and Anderson, Ashton and Zemel, Richard},
  booktitle =    {Proceedings of the 36th International Conference on Machine Learning},
  pages =    {803--811},
  year =   {2019},
  editor =   {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume =   {97},
  series =   {Proceedings of Machine Learning Research},
  address =    {Long Beach, California, USA},
  month =    {09--15 Jun},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v97/brunet19a/brunet19a.pdf},
  url =    {http://proceedings.mlr.press/v97/brunet19a.html},
  abstract =   {Popular word embedding algorithms exhibit stereotypical biases, such as gender bias. The widespread use of these algorithms in machine learning systems can amplify stereotypes in important contexts. Although some methods have been developed to mitigate this problem, how word embedding biases arise during training is poorly understood. In this work we develop a technique to address this question. Given a word embedding, our method reveals how perturbing the training corpus would affect the resulting embedding bias. By tracing the origins of word embedding bias back to the original training documents, one can identify subsets of documents whose removal would most reduce bias. We demonstrate our methodology on Wikipedia and New York Times corpora, and find it to be very accurate.}
}

@inproceedings{Priante2016WhoAmII1,
  title={#WhoAmI in 160 Characters? Classifying Social Identities Based on Twitter Profile Descriptions},
  author={Anna Priante and Djoerd Hiemstra and Tijs van den Broek and Aaqib Saeed and Michel L. Ehrenhard and Ariana Need},
  booktitle={NLP+CSS@EMNLP},
  year={2016}
}

@inproceedings{holgate-etal-2018-swear,
    title = "Why Swear? Analyzing and Inferring the Intentions of Vulgar Expressions",
    author = "Holgate, Eric  and
      Cachola, Isabel  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1471",
    pages = "4405--4414",
    abstract = "Vulgar words are employed in language use for several different functions, ranging from expressing aggression to signaling group identity or the informality of the communication. This versatility of usage of a restricted set of words is challenging for downstream applications and has yet to be studied quantitatively or using natural language processing techniques. We introduce a novel data set of 7,800 tweets from users with known demographic traits where all instances of vulgar words are annotated with one of the six categories of vulgar word use. Using this data set, we present the first analysis of the pragmatic aspects of vulgarity and how they relate to social factors. We build a model able to predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. Finally, we demonstrate the utility of modeling the type of vulgar word use in context by using this information to achieve state-of-the-art performance in hate speech detection on a benchmark data set.",
}

@inproceedings{shin-doyle-2018-alignment,
    title = "Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse",
    author = "Shin, Hagyeong  and
      Doyle, Gabriel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4001",
    doi = "10.18653/v1/N18-4001",
    pages = "1--8",
    abstract = "Conversation is a joint social process, with participants cooperating to exchange information. This process is helped along through linguistic alignment: participants{'} adoption of each other{'}s word use. This alignment is robust, appearing many settings, and is nearly always positive. We create an alignment model for examining alignment in Twitter conversations across antagonistic groups. This model finds that some word categories, specifically pronouns used to establish group identity and common ground, are negatively aligned. This negative alignment is observed despite other categories, which are less related to the group dynamics, showing the standard positive alignment. This suggests that alignment is strongly biased toward cooperative alignment, but that different linguistic features can show substantially different behaviors.",
}

@article{field2019entity,
  title={Entity-Centric Contextual Affective Analysis},
  author={Field, Anjalie and Tsvetkov, Yulia},
  journal={arXiv preprint arXiv:1906.01762},
  year={2019}
}


@inproceedings{ananya-etal-2019-genderquant,
    title = "{G}ender{Q}uant: Quantifying Mention-Level Genderedness",
    author = "{Ananya}  and
      Parthasarthi, Nitya  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1303",
    pages = "2959--2969",
    abstract = "Language is gendered if the context surrounding a mention is suggestive of a particular binary gender for that mention. Detecting the different ways in which language is gendered is an important task since gendered language can bias NLP models (such as for coreference resolution). This task is challenging since genderedness is often expressed in subtle ways. Existing approaches need considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features. Additionally, these approaches do not provide a quantifiable measure of how gendered the text is, nor are they applicable at the fine-grained mention level. In this paper, we use existing NLP pipelines to automatically annotate gender of mentions in the text. On corpora labeled using this method, we train a supervised classifier to predict the gender of any mention from its context and evaluate it on unseen text. The model confidence for a mention{'}s gender can be used as a proxy to indicate the level of genderedness of the context. We test this gendered language detector on movie summaries, movie reviews, news articles, and fiction novels, achieving an AUC-ROC of up to 0.71, and observe that the model predictions agree with human judgments collected for this task. We also provide examples of detected gendered sentences from aforementioned domains.",
}

@article{Manzini2019BlackIT,
  title={Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings},
  author={Thomas Manzini and Yao Chong Lim and Alan W. Black and Yulia Tsvetkov},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.04047}
}

@inproceedings{Bolukbasi:2016:MCP:3157382.3157584,
 author = {Bolukbasi, Tolga and Chang, Kai-Wei and Zou, James and Saligrama, Venkatesh and Kalai, Adam},
 title = {Man is to Computer Programmer As Woman is to Homemaker? Debiasing Word Embeddings},
 booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
 series = {NIPS'16},
 year = {2016},
 isbn = {978-1-5108-3881-9},
 location = {Barcelona, Spain},
 pages = {4356--4364},
 numpages = {9},
 url = {http://dl.acm.org/citation.cfm?id=3157382.3157584},
 acmid = {3157584},
 publisher = {Curran Associates Inc.},
 address = {USA},
} 



@article{Zhao_2017,
   title={Men Also Like Shopping: Reducing Gender Bias Amplification using
            Corpus-level Constraints},
   url={http://dx.doi.org/10.18653/v1/d17-1323},
   DOI={10.18653/v1/d17-1323},
   journal={Proceedings of the 2017 Conference on Empirical Methods in Natural
          Language Processing},
   publisher={Association for Computational Linguistics},
   author={Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
   year={2017}
}

@article{Hendricks_2018,
   title={Women Also Snowboard: Overcoming Bias in Captioning Models},
   ISBN={9783030012199},
   ISSN={1611-3349},
   url={http://dx.doi.org/10.1007/978-3-030-01219-9_47},
   DOI={10.1007/978-3-030-01219-9_47},
   journal={Lecture Notes in Computer Science},
   publisher={Springer International Publishing},
   author={Hendricks, Lisa Anne and Burns, Kaylee and Saenko, Kate and Darrell, Trevor and Rohrbach, Anna},
   year={2018},
   pages={793–811}
}

@inproceedings{Dixon:2018:MMU:3278721.3278729,
 author = {Dixon, Lucas and Li, John and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
 title = {Measuring and Mitigating Unintended Bias in Text Classification},
 booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
 series = {AIES '18},
 year = {2018},
 isbn = {978-1-4503-6012-8},
 location = {New Orleans, LA, USA},
 pages = {67--73},
 numpages = {7},
 url = {http://doi.acm.org/10.1145/3278721.3278729},
 doi = {10.1145/3278721.3278729},
 acmid = {3278729},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {algorithmic bias, fairness, machine learning, natural language processing, text classification},
} 
@article{Garg_2018,
   title={Word embeddings quantify 100 years of gender and ethnic stereotypes},
   volume={115},
   ISSN={1091-6490},
   url={http://dx.doi.org/10.1073/pnas.1720347115},
   DOI={10.1073/pnas.1720347115},
   number={16},
   journal={Proceedings of the National Academy of Sciences},
   publisher={Proceedings of the National Academy of Sciences},
   author={Garg, Nikhil and Schiebinger, Londa and Jurafsky, Dan and Zou, James},
   year={2018},
   month={Apr},
   pages={E3635–E3644}
}

@inproceedings{zhao-etal-2018-gender,
    title = "Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods",
    author = "Zhao, Jieyu  and
      Wang, Tianlu  and
      Yatskar, Mark  and
      Ordonez, Vicente  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2003",
    doi = "10.18653/v1/N18-2003",
    pages = "15--20",
    abstract = "In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.",
}

@misc{abbasi2019fairness,
    title={Fairness in representation: quantifying stereotyping as a representational harm},
    author={Mohsen Abbasi and Sorelle A. Friedler and Carlos Scheidegger and Suresh Venkatasubramanian},
    year={2019},
    eprint={1901.09565},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@inproceedings{agarwal-etal-2019-word,
    title = "Word Embeddings (Also) Encode Human Personality Stereotypes",
    author = "Agarwal, Oshin  and
      Durup{\i}nar, Funda  and
      Badler, Norman I.  and
      Nenkova, Ani",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1023",
    pages = "205--211",
    abstract = "Word representations trained on text reproduce human implicit bias related to gender, race and age. Methods have been developed to remove such bias. Here, we present results that show that human stereotypes exist even for much more nuanced judgments such as personality, for a variety of person identities beyond the typically legally protected attributes and that these are similarly captured in word representations. Specifically, we collected human judgments about a person{'}s Big Five personality traits formed solely from information about the occupation, nationality or a common noun description of a hypothetical person. Analysis of the data reveals a large number of statistically significant stereotypes in people. We then demonstrate the bias captured in lexical representations is statistically significantly correlated with the documented human bias. Our results, showing bias for a large set of person descriptors for such nuanced traits put in doubt the feasibility of broadly and fairly applying debiasing methods and call for the development of new methods for auditing language technology systems and resources.",
}

@article{bordia2019identifying,
  title={Identifying and reducing gender bias in word-level language models},
  author={Bordia, Shikha and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1904.03035},
  year={2019}
}

Cite
BibTex
EndNote
MLA
APA
Chicago

@inproceedings{DeArteaga2019BiasIB,
  title={Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting},
  author={Maria De-Arteaga and Alexey Romanov and Hanna M. Wallach and Jennifer T. Chayes and Christian Borgs and Alexandra Chouldechova and Sahin Cem Geyik and Krishnaram Kenthapadi and Adam Tauman Kalai},
  booktitle={FAT},
  year={2019}
}

@article{stanovsky2019evaluating,
  title={Evaluating Gender Bias in Machine Translation},
  author={Stanovsky, Gabriel and Smith, Noah A and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:1906.00591},
  year={2019}
}


@inproceedings{mimno_strange_2017-1,
  title = {The Strange Geometry of Skip-Gram with Negative Sampling},
  booktitle = {Proceedings of the 2017 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Mimno, David and Thompson, Laure},
  year = {2017},
  pages = {2873--2878}
}




@inproceedings{Danks:2017:ABA:3171837.3171944,
 author = {Danks, David and London, Alex John},
 title = {Algorithmic Bias in Autonomous Systems},
 booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence},
 series = {IJCAI'17},
 year = {2017},
 isbn = {978-0-9992411-0-3},
 location = {Melbourne, Australia},
 pages = {4691--4697},
 numpages = {7},
 url = {http://dl.acm.org/citation.cfm?id=3171837.3171944},
 acmid = {3171944},
 publisher = {AAAI Press},
} 

@article{martell_male-female_1996,
  title = {Male-Female Differences: A Computer Simulation.},
  volume = {51},
  shorttitle = {Male-Female Differences},
  journal = {American Psychologist},
  author = {Martell, Richard F. and Lane, David M. and Emrich, Cynthia},
  year = {1996},
  pages = {157-158}
}




@inproceedings{Zhao2018LearningGW,
  title={Learning Gender-Neutral Word Embeddings},
  author={Jieyu Zhao and Yichao Zhou and Zeyu Li and Wei Wang and Kai-Wei Chang},
  booktitle={EMNLP},
  year={2018}
}

@misc{yang2019xlnet,
    title={XLNet: Generalized Autoregressive Pretraining for Language Understanding},
    author={Zhilin Yang and Zihang Dai and Yiming Yang and Jaime Carbonell and Ruslan Salakhutdinov and Quoc V. Le},
    year={2019},
    eprint={1906.08237},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{alex2019unsupervised,
    title={Unsupervised Discovery of Gendered Language through Latent-Variable Modeling},
    author={Alexander Hoyle and Wolf-Sonkin and Hanna Wallach and Isabelle Augenstein and Ryan Cotterell},
    year={2019},
    eprint={1906.04760},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{kurita2019measuring,
    title={Measuring Bias in Contextualized Word Representations},
    author={Keita Kurita and Nidhi Vyas and Ayush Pareek and Alan W Black and Yulia Tsvetkov},
    year={2019},
    eprint={1906.07337},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@article{Lee_2018,
   title={Higher-Order Coreference Resolution with Coarse-to-Fine Inference},
   url={http://dx.doi.org/10.18653/v1/N18-2108},
   DOI={10.18653/v1/n18-2108},
   journal={Proceedings of the 2018 Conference of the North American Chapter of
          the Association for Computational Linguistics: Human Language
          Technologies, Volume 2 (Short Papers)},
   publisher={Association for Computational Linguistics},
   author={Lee, Kenton and He, Luheng and Zettlemoyer, Luke},
   year={2018}
}



@inproceedings{Yin:2018:DWE:3326943.3327026,
 author = {Yin, Zi and Shen, Yuanyuan},
 title = {On the Dimensionality of Word Embedding},
 booktitle = {Proceedings of the 32Nd International Conference on Neural Information Processing Systems},
 series = {NIPS'18},
 year = {2018},
 location = {Montr\&\#233;al, Canada},
 pages = {895--906},
 numpages = {12},
 url = {http://dl.acm.org/citation.cfm?id=3326943.3327026},
 acmid = {3327026},
 publisher = {Curran Associates Inc.},
 address = {USA},
} 

@article{levy-etal-2015-improving,
    title = "Improving Distributional Similarity with Lessons Learned from Word Embeddings",
    author = "Levy, Omer  and
      Goldberg, Yoav  and
      Dagan, Ido",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1016",
    doi = "10.1162/tacl_a_00134",
    pages = "211--225",
    abstract = "Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.",
}

@inproceedings{ficler-goldberg-2017-controlling,
    title = "Controlling Linguistic Style Aspects in Neural Language Generation",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4912",
    doi = "10.18653/v1/W17-4912",
    pages = "94--104",
    abstract = "Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.",
}

@inproceedings{herzig-etal-2017-neural,
    title = "Neural Response Generation for Customer Service based on Personality Traits",
    author = "Herzig, Jonathan  and
      Shmueli-Scheuer, Michal  and
      Sandbank, Tommy  and
      Konopnicki, David",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3541",
    doi = "10.18653/v1/W17-3541",
    pages = "252--256",
    abstract = "We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.",
}

@inproceedings{li-etal-2018-delete,
    title = "Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer",
    author = "Li, Juncen  and
      Jia, Robin  and
      He, He  and
      Liang, Percy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1169",
    doi = "10.18653/v1/N18-1169",
    pages = "1865--1874",
    abstract = "We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., {``}screen is just the right size{''} to {``}screen is too small{''}). Our training data includes only sentences labeled with their attribute (e.g., positive and negative), but not pairs of sentences that only differ in the attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., {``}too small{''}). Our strongest method extracts content words by deleting phrases associated with the sentence{'}s original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. Based on human evaluation, our best method generates grammatical and appropriate responses on 22{\%} more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.",
}


@inproceedings{Yu2017SeqGANSG,
  title={SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient},
  author={Lantao Yu and Weinan Zhang and Jun Wang and Yong Yu},
  booktitle={AAAI},
  year={2017}
}
@inproceedings{clark-etal-2018-neural,
    title = "Neural Text Generation in Stories Using Entity Representations as Context",
    author = "Clark, Elizabeth  and
      Ji, Yangfeng  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1204",
    doi = "10.18653/v1/N18-1204",
    pages = "2250--2260",
    abstract = "We introduce an approach to neural text generation that explicitly represents entities mentioned in the text. Entity representations are vectors that are updated as the text proceeds; they are designed specifically for narrative text like fiction or news stories. Our experiments demonstrate that modeling entities offers a benefit in two automatic evaluations: mention generation (in which a model chooses which entity to mention next and which words to use in the mention) and selection between a correct next sentence and a distractor from later in the same story. We also conduct a human evaluation on automatically generated text in story contexts; this study supports our emphasis on entities and suggests directions for further research.",
}

@inproceedings{Bowman2016GeneratingSF,
  title={Generating Sentences from a Continuous Space},
  author={Samuel R. Bowman and Luke Vilnis and Oriol Vinyals and Andrew M. Dai and Rafal J{\'o}zefowicz and Samy Bengio},
  booktitle={CoNLL},
  year={2016}
}

@misc{kim2018tutorial,
    title={A Tutorial on Deep Latent Variable Models of Natural Language},
    author={Yoon Kim and Sam Wiseman and Alexander M. Rush},
    year={2018},
    eprint={1812.06834},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@inproceedings{wang-etal-2019-topic,
    title = "Topic-Guided Variational Auto-Encoder for Text Generation",
    author = "Wang, Wenlin  and
      Gan, Zhe  and
      Xu, Hongteng  and
      Zhang, Ruiyi  and
      Wang, Guoyin  and
      Shen, Dinghan  and
      Chen, Changyou  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1015",
    pages = "166--177",
    abstract = "We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides a guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during the model inference. Experimental results show that our TGVAE outperforms its competitors on both unconditional and conditional text generation, which can also generate semantically-meaningful sentences with various topics.",
}

@article{Sutton1988ReinforcementLA,
  title={Reinforcement Learning: An Introduction},
  author={Richard S. Sutton and Andrew G. Barto},
  journal={IEEE Transactions on Neural Networks},
  year={1988},
  volume={16},
  pages={285-286}
}

@inproceedings{Narasimhan2015LanguageUF,
  title={Language Understanding for Text-based Games using Deep Reinforcement Learning},
  author={Kumaravelu Narasimhan and Tejas D. Kulkarni and Regina Barzilay},
  booktitle={EMNLP},
  year={2015}
}

@inproceedings{Qin2018RobustDS,
  title={Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning},
  author={Pengda Qin and Weiran Xu and William Yang Wang},
  booktitle={ACL},
  year={2018}
}

@inproceedings{Fang2017LearningHT,
  title={Learning how to Active Learn: A Deep Reinforcement Learning Approach},
  author={Meng Fang and Christy Y. Li and Trevor Cohn},
  booktitle={EMNLP},
  year={2017}
}

@inproceedings{Lin2018MultiHopKG,
  title={Multi-Hop Knowledge Graph Reasoning with Reward Shaping},
  author={Xi Victoria Lin and Richard Socher and Caiming Xiong},
  booktitle={EMNLP},
  year={2018}
}

@inproceedings{lewis-etal-2017-deal,
    title = "Deal or No Deal? End-to-End Learning of Negotiation Dialogues",
    author = "Lewis, Mike  and
      Yarats, Denis  and
      Dauphin, Yann  and
      Parikh, Devi  and
      Batra, Dhruv",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1259",
    doi = "10.18653/v1/D17-1259",
    pages = "2443--2453",
    abstract = "Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other{'}s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.",
}

@inproceedings{Li2016DeepRL,
  title={Deep Reinforcement Learning for Dialogue Generation},
  author={Jiwei Li and Will Monroe and Alan Ritter and Daniel Jurafsky and Michel Galley and Jianfeng Gao},
  booktitle={EMNLP},
  year={2016}
}

@inproceedings{Li2018LearningAN,
  title={Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient},
  author={Kam Cheong Li and Jieping Ye},
  booktitle={KDD},
  year={2018}
}

@inproceedings{He2018DecouplingSA,
  title={Decoupling Strategy and Generation in Negotiation Dialogues},
  author={He He and Derek Chen and Anusha Balakrishnan and Percy S. Liang},
  booktitle={EMNLP},
  year={2018}
}

@inproceedings{mao-etal-2018-end,
    title = "End-to-End Reinforcement Learning for Automatic Taxonomy Induction",
    author = "Mao, Yuning  and
      Ren, Xiang  and
      Shen, Jiaming  and
      Gu, Xiaotao  and
      Han, Jiawei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1229",
    pages = "2462--2472",
    abstract = "We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (\textit{i.e.},, detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6{\%} on ancestor F1.",
}

@article{Hoey2016AffectCP,
  title={Affect control processes: Intelligent affective interaction using a partially observable Markov decision process},
  author={Jesse Hoey and Tobias Schr{\"o}der and Areej Alhothali},
  journal={Artif. Intell.},
  year={2016},
  volume={230},
  pages={134-172}
}

@article{Swinger_2019,
   title={What are the Biases in My Word Embedding?},
   ISBN={9781450363242},
   url={http://dx.doi.org/10.1145/3306618.3314270},
   DOI={10.1145/3306618.3314270},
   journal={Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society  - AIES  ’19},
   publisher={ACM Press},
   author={Swinger, Nathaniel and De-Arteaga, Maria and Heffernan IV, Neil Thomas and Leiserson, Mark DM and Kalai, Adam Tauman},
   year={2019}
}

@incollection{RobinsonAffectControlTheory2006,
  series = {Handbooks of {{Sociology}} and {{Social Research}}},
  title = {Affect {{Control Theory}}},
  copyright = {\textcopyright{}2006 Springer Science+Business Media, LLC},
  isbn = {978-0-387-30713-8 978-0-387-30715-2},
  abstract = {When David Heise (1977,1979) published his early statements of affect control theory, contribut-ing to the newly developing sociology of emotion was not his primary goal. The main objective of the theory was to explain behavior in the context of social interactions. Heise hoped to develop a formal framework that could describe both the routine, expected role behaviors that people enact under normal circumstances and the creative responses they generate when encountering noninstitutionalized or counternormative situations. He combined insights from a measurement tradition in psycholinguistics (Osgood 1962, 1966; Osgood et al. 1973, 1975), empirical studies of impression formation (Gollob 1968; Gollob and Rossman 1973; Heise 1969, 1970), and a cybernetic model of perception (Powers 1973) to create his new theory of social action.},
  language = {en},
  booktitle = {Handbook of the {{Sociology}} of {{Emotions}}},
  publisher = {{Springer US}},
  author = {Robinson, Dawn T. and {Smith-Lovin}, Lynn and Wisecup, Allison K.},
  month = jan,
  year = {2006},
  keywords = {Sociology,Personality \& Social Psychology},
  pages = {179-202}
}

@article{wagner_women_2016,
  title = {Women through the Glass Ceiling: Gender Asymmetries in {{Wikipedia}}},
  volume = {5},
  copyright = {2016 Wagner et al.},
  shorttitle = {Women through the Glass Ceiling},
  abstract = {Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world's knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation. We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.},
  language = {En},
  number = {1},
  journal = {EPJ Data Science},
  author = {Wagner, Claudia and {Graells-Garrido}, Eduardo and Garcia, David and Menczer, Filippo},
  month = dec,
  year = {2016},
  pages = {5}
}

@techreport{spirling2019word,
  title={Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research},
  author={Spirling, Arthur and Rodriguez, P},
  year={2019},
  institution={Working paper}
}

@article{suresh_framework_2019,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1901.10002},
  primaryClass = {cs, stat},
  title = {A {{Framework}} for {{Understanding Unintended Consequences}} of {{Machine Learning}}},
  abstract = {As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of how and why unwanted consequences arise. For instance, downstream harms to particular groups are often blamed on "biased data," but this concept encompass too many issues to be useful in developing solutions. In this paper, we provide a framework that partitions sources of downstream harm in machine learning into five distinct categories spanning the data generation and machine learning pipeline. We describe how these issues arise, how they are relevant to particular applications, and how they motivate different solutions. In doing so, we aim to facilitate the development of solutions that stem from an understanding of application-specific populations and data generation processes, rather than relying on general claims about what may or may not be "fair."},
  journal = {arXiv:1901.10002 [cs, stat]},
  author = {Suresh, Harini and Guttag, John V.},
  month = jan,
  year = {2019},
  keywords = {Statistics - Machine Learning,Computer Science - Machine Learning}
}


@techreport{olteanu_social_2016,
  address = {{Rochester, NY}},
  type = {{{SSRN Scholarly Paper}}},
  title = {Social {{Data}}: {{Biases}}, {{Methodological Pitfalls}}, and {{Ethical Boundaries}}},
  shorttitle = {Social {{Data}}},
  abstract = {Social data in digital form, which includes user-generated content, expressed or implicit relationships between people, and behavioral traces, are at the core of many popular applications and platforms, driving the research agenda of many researchers.  The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, product, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics.  Many academics and practitioners have warned against the na{\"i}ve usage of social data.  There are biases and inaccuracies occurring at the source of the data, but also introduced during processing.  There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked.  This survey recognizes the rigor with which these issues are addressed by different researchers varies across a wide range.  We present a framework for identifying a broad variety of menaces in the research and practices around social data use.},
  language = {en},
  number = {ID 2886526},
  institution = {{Social Science Research Network}},
  author = {Olteanu, Alexandra and Castillo, Carlos and Diaz, Fernando and Kiciman, Emre},
  month = dec,
  year = {2016},
  keywords = {evaluation,user-generated content,Social media,behavioral traces,data biases,ethical challanges}
}
@article{faruqui2016problems,
  title={Problems with evaluation of word embeddings using word similarity tasks},
  author={Faruqui, Manaal and Tsvetkov, Yulia and Rastogi, Pushpendre and Dyer, Chris},
  journal={arXiv preprint arXiv:1605.02276},
  year={2016}
}


@article{faruqui_retrofitting_2014,
  title = {Retrofitting Word Vectors to Semantic Lexicons},
  journal = {arXiv preprint arXiv:1411.4166},
  author = {Faruqui, Manaal and Dodge, Jesse and Jauhar, Sujay K. and Dyer, Chris and Hovy, Eduard and Smith, Noah A.},
  year = {2014}
}

@article{faruqui_non-distributional_2015,
  title = {Non-Distributional Word Vector Representations},
  journal = {arXiv preprint arXiv:1506.05230},
  author = {Faruqui, Manaal and Dyer, Chris},
  year = {2015}
}




@inproceedings{danks_algorithmic_2017-1,
  title = {Algorithmic {{Bias}} in {{Autonomous Systems}}.},
  booktitle = {{{IJCAI}}},
  author = {Danks, David and London, Alex John},
  year = {2017},
  pages = {4691--4697}
}




@article{silva_algorithms_2018,
  title = {Algorithms, {{Platforms}}, and {{Ethnic Bias}}: {{An Integrative Essay}}},
  volume = {55},
  shorttitle = {Algorithms, {{Platforms}}, and {{Ethnic Bias}}},
  abstract = {[Racially biased outcomes have increasingly been recognized as a problem that can infect software algorithms and datasets of all types. Digital platforms, in particular, are organizing ever greater portions of social, political, and economic life. This essay examines and organizes current academic and popular press discussions on how digital tools, despite appearing to be objective We have modified and extended a "value chain\textendash{}like" model introduced by Danks and London (2017), depicting the potential location of ethnic bias in algorithmic decision-making. In our discussion, we note some potential benefits from the movement of decisions online, as they are then traceable and amenable to analysis.]},
  number = {1 \& 2},
  journal = {Phylon (1960-)},
  author = {Silva, Selena and Kenney, Martin},
  year = {2018},
  pages = {9-37}
}


@inproceedings{fast_shirtless_2016-1,
  title = {Shirtless and Dangerous: {{Quantifying}} Linguistic Signals of Gender Bias in an Online Fiction Writing Community},
  shorttitle = {Shirtless and Dangerous},
  booktitle = {Tenth {{International AAAI Conference}} on {{Web}} and {{Social Media}}},
  author = {Fast, Ethan and Vachovsky, Tina and Bernstein, Michael S.},
  year = {2016}
}





@inproceedings{LevyDependencybasedwordembeddings2014,
  title = {Dependencybased Word Embeddings},
  volume = {2},
  booktitle = {Proceedings of the 52nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}},
  author = {Levy, Omer and Goldberg, Yoav},
  year = {2014},
  pages = {302--308}
}



@inproceedings{wagner_its_2015-1,
  title = {It's a Man's {{Wikipedia}}? {{Assessing}} Gender Inequality in an Online Encyclopedia},
  shorttitle = {It's a Man's {{Wikipedia}}?},
  booktitle = {Ninth International {{AAAI}} Conference on Web and Social Media},
  author = {Wagner, Claudia and Garcia, David and Jadidi, Mohsen and Strohmaier, Markus},
  year = {2015}
}


@inproceedings{LiLiuEtAl_2017_Investigating_Different_Syntactic_Context_Types_and_Context_Representations_for_Learning_Word_Embeddings,
 title = {Investigating {{Different Syntactic Context Types}} and {{Context Representations}} for {{Learning Word Embeddings}}},
 url = {http://www.aclweb.org/anthology/D17-1256},
 booktitle = {Proceedings of the 2017 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
 author = {Li, Bofang and Liu, Tao and Zhao, Zhe and Tang, Buzhou and Drozd, Aleksandr and Rogers, Anna and Du, Xiaoyong},
 year = {2017},
 pages = {2411--2421}}

@article{knowles2005white,
  title={White selves: conceptualizing and measuring a dominant-group identity.},
  author={Knowles, Eric D and Peng, Kaiping},
  journal={Journal of personality and social psychology},
  volume={89},
  number={2},
  pages={223},
  year={2005},
  publisher={American Psychological Association}
}

@inproceedings{swinger_what_2019,
  address = {{New York, NY, USA}},
  series = {{{AIES}} '19},
  title = {What {{Are}} the {{Biases}} in {{My Word Embedding}}?},
  isbn = {978-1-4503-6324-2},
  abstract = {This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "debiased" embedding. These biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case tokens. The algorithm is highly unsupervised: it does not even require the sensitive features to be pre-specified. This is desirable because: (a) many forms of discrimination?such as racial discrimination-are linked to social constructs that may vary depending on the context, rather than to categories with fixed definitions; and (b) it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding. It outputs a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.},
  booktitle = {Proceedings of the 2019 {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
  publisher = {{ACM}},
  author = {Swinger, Nathaniel and {De-Arteaga}, Maria and Heffernan IV, Neil Thomas and Leiserson, Mark DM and Kalai, Adam Tauman},
  year = {2019},
  keywords = {bias,fairness,word embeddings},
  pages = {305--311}
}



@unpublished{joseph_identity_2018-1,
  title = {The {{Identity Labeling Problem}} and a {{Corresponding Solution}}},
  abstract = {The present work introduces and mathematically formalizes the identity labeling problem - given an individual in a social situatio, can we predict what identity(ies) he or she will be labeled with by someone else (the ``labeler'')? We argue that existing predictive models of identity labeling are theoretically incomplete and provide results from a survey-based stimuli that confirm this intuition. We then introduce a novel approach to the identity labeling problem, which we call Latent Cognitive Social Spaces (LCSS). We demonstrate that LCSS is a better predictor of identity labeling in survey data than previous models and discuss its theoretical implications for linking the definition of the situation to identity theories. 
    Hosted on the Open Science Framework},
  language = {en},
  author = {Joseph, Kenneth and Morgan, Jonathan},
  month = jul,
  year = {2018}
}




@article{todorov_social_2015,
  title = {Social {{Attributions}} from {{Faces}}: {{Determinants}}, {{Consequences}}, {{Accuracy}}, and {{Functional Significance}}},
  volume = {66},
  shorttitle = {Social {{Attributions}} from {{Faces}}},
  abstract = {Since the early twentieth century, psychologists have known that there is consensus in attributing social and personality characteristics from facial appearance. Recent studies have shown that surprisingly little time and effort are needed to arrive at this consensus. Here we review recent research on social attributions from faces. Section I outlines data-driven methods capable of identifying the perceptual basis of consensus in social attributions from faces (e.g., What makes a face look threatening?). Section II describes nonperceptual determinants of social attributions (e.g., person knowledge and incidental associations). Section III discusses evidence that attributions from faces predict important social outcomes in diverse domains (e.g., investment decisions and leader selection). In Section IV, we argue that the diagnostic validity of these attributions has been greatly overstated in the literature. In the final section, we offer an account of the functional significance of these attributions.},
  number = {1},
  journal = {Annual Review of Psychology},
  author = {Todorov, Alexander and Olivola, Christopher Y. and Dotsch, Ron and {Mende-Siedlecki}, Peter},
  year = {2015},
  pages = {519-545},
  pmid = {25196277}
}



@book{OsgoodCrossculturaluniversalsaffective1975,
  title = {Cross-Cultural Universals of Affective Meaning},
  publisher = {{University of Illinois Press}},
  author = {Osgood, Charles Egerton},
  year = {1975}
}


@inproceedings{brunet_understanding_2019,
  title = {Understanding the {{Origins}} of {{Bias}} in {{Word Embeddings}}},
  abstract = {Popular word embedding algorithms exhibit stereotypical biases, such as gender bias. The widespread use of these algorithms in machine learning systems can amplify stereotypes in important contexts...},
  language = {en},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  author = {Brunet, Marc-Etienne and {Alkalay-Houlihan}, Colleen and Anderson, Ashton and Zemel, Richard},
  month = may,
  year = {2019},
  pages = {803-811}
}
@inproceedings{lauscher_are_2019,
  title={Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors},
  author={Lauscher, Anne and Glava{\v{s}}, Goran},
  booktitle={Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (* SEM 2019)},
  pages={85--91},
  year={2019}
}

@inproceedings{chaloner_measuring_2019,
  address = {{Florence, Italy}},
  title = {Measuring {{Gender Bias}} in {{Word Embeddings}} across {{Domains}} and {{Discovering New Gender Bias Word Categories}}},
  abstract = {Prior work has shown that word embeddings capture human stereotypes, including gender bias. However, there is a lack of studies testing the presence of specific gender bias categories in word embeddings across diverse domains. This paper aims to fill this gap by applying the WEAT bias detection method to four sets of word embeddings trained on corpora from four different domains: news, social networking, biomedical and a gender-balanced corpus extracted from Wikipedia (GAP). We find that some domains are definitely more prone to gender bias than others, and that the categories of gender bias present also vary for each set of word embeddings. We detect some gender bias in GAP. We also propose a simple but novel method for discovering new bias categories by clustering word embeddings. We validate this method through WEAT's hypothesis testing mechanism and find it useful for expanding the relatively small set of well-known gender bias word categories commonly used in the literature.},
  booktitle = {Proceedings of the {{First Workshop}} on {{Gender Bias}} in {{Natural Language Processing}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Chaloner, Kaytlin and Maldonado, Alfredo},
  month = aug,
  year = {2019},
  pages = {25--32}
}




@article{fiske_model_2002,
  title = {A Model of (Often Mixed) Stereotype Content: Competence and Warmth Respectively Follow from Perceived Status and Competition.},
  volume = {82},
  shorttitle = {A Model of (Often Mixed) Stereotype Content},
  number = {6},
  journal = {Journal of personality and social psychology},
  author = {Fiske, Susan T. and Cuddy, Amy JC and Glick, Peter and Xu, Jun},
  year = {2002},
  pages = {878}
}



@article{freeman_neural_2018,
  title = {The Neural Representational Geometry of Social Perception},
  journal = {Current opinion in psychology},
  author = {Freeman, Jonathan B. and Stolier, Ryan M. and Brooks, Jeffrey A. and Stillerman, Benjamin A.},
  year = {2018}
}



@inproceedings{kelley1967attribution,
  title={Attribution theory in social psychology.},
  author={Kelley, Harold H},
  booktitle={Nebraska symposium on motivation},
  year={1967},
  organization={University of Nebraska Press}
}
@article{smith-lovin_affect_1992,
  title = {An Affect Control Analysis of Two Religious Subcultures},
  volume = {1},
  journal = {Social perspectives on emotion},
  author = {{Smith-Lovin}, L. and Douglas, W.},
  year = {1992},
  pages = {217--47}
}


@techreport{spirling_word_2019,
  title = {Word {{Embeddings}}: {{What}} Works, What Doesn't, and How to Tell the Difference for Applied Research},
  shorttitle = {Word {{Embeddings}}},
  institution = {{Working paper}},
  author = {Spirling, Arthur and Rodriguez, P.},
  year = {2019}
}




@book{heise_surveying_2010,
  title = {Surveying Cultures: {{Discovering}} Shared Conceptions and Sentiments},
  shorttitle = {Surveying Cultures},
  publisher = {{John Wiley \& Sons}},
  author = {Heise, David R.},
  year = {2010}
}




@inproceedings{pennington2014glove,
  author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning},
  booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
  title = {GloVe: Global Vectors for Word Representation},
  year = {2014},
  pages = {1532--1543},
  url = {http://www.aclweb.org/anthology/D14-1162},
}


@misc{dev2019attenuating,
    title={Attenuating Bias in Word Vectors},
    author={Sunipa Dev and Jeff Phillips},
    year={2019},
    eprint={1901.07656},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@article{Caliskan_2017,
   title={Semantics derived automatically from language corpora contain human-like biases},
   volume={356},
   ISSN={1095-9203},
   url={http://dx.doi.org/10.1126/science.aal4230},
   DOI={10.1126/science.aal4230},
   number={6334},
   journal={Science},
   publisher={American Association for the Advancement of Science (AAAS)},
   author={Caliskan, Aylin and Bryson, Joanna J. and Narayanan, Arvind},
   year={2017},
   month={Apr},
   pages={183–186}
}


@article{garg_word_2018,
  title = {Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes},
  volume = {115},
  copyright = {\textcopyright{} 2018 . http://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license.},
  abstract = {Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts\textemdash{}e.g., the women's movement in the 1960s and Asian immigration into the United States\textemdash{}and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.},
  language = {en},
  number = {16},
  journal = {Proceedings of the National Academy of Sciences},
  author = {Garg, Nikhil and Schiebinger, Londa and Jurafsky, Dan and Zou, James},
  month = apr,
  year = {2018},
  keywords = {ethnic stereotypes,gender stereotypes,word embedding},
  pages = {E3635-E3644},
  pmid = {29615513}
}




@inproceedings{ethayarajh-etal-2019-understanding,
    title = "Understanding Undesirable Word Embedding Associations",
    author = "Ethayarajh, Kawin  and
      Duvenaud, David  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1166",
    pages = "1696--1705",
    abstract = "Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.",
}

@article{heise_affect_1987,
  title = {Affect Control Theory: {{Concepts}} and Model},
  volume = {13},
  shorttitle = {Affect Control Theory},
  abstract = {A current statement of affect control theory is provided with attention to the qualitative framework for analyses and the quantitative variables and processes involved in affective dynamics. The theory is discussed with regard to emotions, event likelihoods, labeling and attribution, and deviance and social control. The mathematical model is presented including event-construction equations derived from event-reaction equations, and attribution equations derived from equations for amalgamating identities with person modifiers. A brief overview of empirical research concerning the theory concludes the article.},
  number = {1-2},
  journal = {The Journal of Mathematical Sociology},
  author = {Heise, David R.},
  month = dec,
  year = {1987},
  pages = {1-33}
}

@article{cikara_neuroscience_2014,
  title = {The {{Neuroscience}} of {{Intergroup Relations An Integrative Review}}},
  volume = {9},
  number = {3},
  journal = {Perspectives on Psychological Science},
  author = {Cikara, Mina and Van Bavel, Jay J.},
  year = {2014},
  keywords = {Cognition,Intergroup relations,neuroscience,social cognition},
  pages = {245--274}
}

@article{smith-lovin_affect_1992,
  title = {An Affect Control Analysis of Two Religious Subcultures},
  volume = {1},
  journal = {Social perspectives on emotion},
  author = {{Smith-Lovin}, L. and Douglas, W.},
  year = {1992},
  pages = {217--47}
}

@article{agresti_approximate_1998,
  title = {Approximate {{Is Better}} than "{{Exact}}" for {{Interval Estimation}} of {{Binomial Proportions}}},
  volume = {52},
  abstract = {For interval estimation of a proportion, coverage probabilities tend to be too large for "exact" confidence intervals based on inverting the binomial test and too small for the interval based on inverting the Wald large-sample normal test (i.e., sample proportion {$\pm$} z-score \texttimes{} estimated standard error). Wilson's suggestion of inverting the related score test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes. The 95\% score interval has similar behavior as the adjusted Wald interval obtained after adding two "successes" and two "failures" to the sample. In elementary courses, with the score and adjusted Wald methods it is unnecessary to provide students with awkward sample size guidelines.},
  number = {2},
  journal = {The American Statistician},
  author = {Agresti, Alan and Coull, Brent A.},
  month = may,
  year = {1998},
  pages = {119-126},
  note = {ArticleType: research-article / Full publication date: May, 1998 / Copyright \textcopyright{} 1998 American Statistical Association}
}

@inproceedings{owoputi_improved_2013,
  title = {Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters},
  booktitle = {Proceedings of {{NAACL}}},
  author = {Owoputi, Olutobi and O'Connor, Brendan and Dyer, Chris and Gimpel, Kevin and Schneider, Nathan and Smith, Noah A},
  year = {2013}
}

@article{tajfel_social_1971,
  title = {Social Categorization and Intergroup Behaviour},
  volume = {1},
  number = {2},
  journal = {European journal of social psychology},
  author = {Tajfel, Henri and Billig, Michael G. and Bundy, Robert P. and Flament, Claude},
  year = {1971},
  pages = {149--178}
}

@book{heise_understanding_1979,
  title = {Understanding Events: {{Affect}} and the Construction of Social Action},
  shorttitle = {Understanding Events},
  publisher = {{CUP Archive}},
  author = {Heise, David R.},
  year = {1979}
}

@article{mcfadden_econometric_1980,
  title = {Econometric Models for Probabilistic Choice among Products},
  journal = {Journal of Business},
  author = {McFadden, D.},
  year = {1980},
  pages = {13--29}
}

@article{fiske_model_2002,
  title = {A Model of (Often Mixed) Stereotype Content: Competence and Warmth Respectively Follow from Perceived Status and Competition.},
  volume = {82},
  shorttitle = {A Model of (Often Mixed) Stereotype Content},
  number = {6},
  journal = {Journal of personality and social psychology},
  author = {Fiske, Susan T. and Cuddy, Amy JC and Glick, Peter and Xu, Jun},
  year = {2002},
  pages = {878}
}




@article{hill_simlex-999_2014,
  title = {Simlex-999: {{Evaluating}} Semantic Models with (Genuine) Similarity Estimation},
  volume = {41},
  shorttitle = {Simlex-999},
  number = {4},
  journal = {Computational Linguistics},
  author = {Hill, Felix and Reichart, Roi and Korhonen, Anna},
  year = {2015},
  pages = {665--695}
}





@article{freeman_dynamic_2011,
  title = {A Dynamic Interactive Theory of Person Construal.},
  volume = {118},
  number = {2},
  journal = {Psychological review},
  author = {Freeman, Jonathan B. and Ambady, Nalini},
  year = {2011},
  pages = {247}
}

@article{smith-lovin_interpreting_2015,
  title = {Interpreting and {{Responding}} to {{Events}} in {{Arabic Culture}}},
  journal = {Final Report to Office of Naval Research, Grant N00014-09-1-0556},
  author = {{Smith-Lovin}, L. and Robinson, Dawn T.},
  year = {2015}
}

@book{heise_self_2010,
  title = {Self, Identity, and Social Institutions},
  publisher = {{Palgrave Macmillan}},
  author = {MacKinnon, Neil J. and Heise, David R.},
  year = {2010}
}

@article{rogers_affective_2013,
  title = {The {{Affective Structure}} of {{Stereotype Content Behavior}} and {{Emotion}} in {{Intergroup Context}}},
  volume = {76},
  abstract = {Affect control theory and the stereotype content model share explanatory goals and employ compatible measurement strategies but have developed in largely separate literatures. The present article examines the models' commensurability and discusses new insights that can be gained by comparing theories. We first demonstrate that the unique measurement dimensions used by each theory (evaluation/potency/activity vs. warmth/competence) describe much of the same semantic content. We then show how simulation techniques developed by affect control theorists can be applied to the study of interactions with stereotyped groups. These simulations indicate broad consistencies between the theories' predictions but highlight three distinctive emphases of affect control theory. Specifically, affect control models predict that actors are motivated to behave in ways that (1) are consistent with self-meanings, (2) maintain cultural norms about the suitability of behaviors and emotions to role relations, and (3) account for behavior and emotion in prior interactions.},
  language = {en},
  number = {2},
  journal = {Social Psychology Quarterly},
  author = {Rogers, Kimberly B. and Schr\"oder, Tobias and Scholl, Wolfgang},
  month = jun,
  year = {2013},
  keywords = {affect control theory,Emotion,Intergroup relations,social behavior,stereotype content},
  pages = {125-150}
}

@article{Smith-Lovinaffectivecontrolevents1987,
  title = {The Affective Control of Events within Settings*},
  volume = {13},
  number = {1-2},
  journal = {Journal of Mathematical Sociology},
  author = {{Smith-Lovin}, Lynn},
  year = {1987},
  pages = {71--101}
}



@article{freeland_structure_2018,
  title = {The Structure of Deference: {{Modeling}} Occupational Status Using Affect Control Theory},
  volume = {83},
  shorttitle = {The Structure of Deference},
  number = {2},
  journal = {American Sociological Review},
  author = {Freeland, Robert E. and Hoey, Jesse},
  year = {2018},
  pages = {243--277}
}

@article{gunning_explainable_2017,
  title = {Explainable Artificial Intelligence (Xai)},
  journal = {Defense Advanced Research Projects Agency (DARPA), nd Web},
  author = {Gunning, David},
  year = {2017}
}



@book{lopez_dog_2015,
  title = {Dog Whistle Politics: {{How}} Coded Racial Appeals Have Reinvented Racism and Wrecked the Middle Class},
  shorttitle = {Dog Whistle Politics},
  publisher = {{Oxford University Press}},
  author = {L\'opez, Ian Haney},
  year = {2015}
}




@article{joseph_social-event_2016,
  title = {A Social-Event Based Approach to Sentiment Analysis of Identities and Behaviors in Text},
  journal = {The Journal of Mathematical Sociology},
  author = {Joseph, Kenneth and Wei, Wei and Benigni, Matthew and Carley, Kathleen M.},
  year = {2016},
  pages = {1--30}
}




@inproceedings{hoey_bayesian_2013,
  title = {Bayesian {{Affect Control Theory}}},
  abstract = {Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.},
  booktitle = {2013 {{Humaine Association Conference}} on {{Affective Computing}} and {{Intelligent Interaction}} ({{ACII}})},
  author = {Hoey, J. and Schroder, T. and Alhothali, A},
  month = sep,
  year = {2013},
  keywords = {Aerospace electronics,affectively believable interactions,Bayes methods,Bayesian affect control theory,behaviour identity,behavioural sciences computing,Control theory,Cultural differences,cultural emotional sentiments,Equations,human-interactive capability,intelligent tutoring application,intelligent tutoring systems,Mathematical model,mathematical representation,maximum likelihood estimation,maximum likelihood solution,noise,optimal behaviour prediction,optimal identity prediction,person interactions,probabilistic decision theoretic model,Probability distribution,Psychology,statistical distributions,Transient analysis,transient emotional sentiments},
  pages = {166-172}
}

@article{osgood_whys_1969,
  title = {On the Whys and Wherefores of {{E}}, {{P}}, and {{A}}.},
  volume = {12},
  number = {3},
  journal = {Journal of Personality and Social Psychology},
  author = {Osgood, Charles E.},
  year = {1969},
  pages = {194}
}

@article{merton_role-set:_1957,
  title = {The Role-Set: {{Problems}} in Sociological Theory},
  volume = {8},
  shorttitle = {The Role-Set},
  number = {2},
  journal = {The British Journal of Sociology},
  author = {Merton, Robert K.},
  year = {1957},
  pages = {106-120}
}

@article{bolukbasi_man_2016-2,
  title = {Man Is to {{Computer Programmer}} as {{Woman}} Is to {{Homemaker}}? {{Debiasing Word Embeddings}}},
  shorttitle = {Man Is to {{Computer Programmer}} as {{Woman}} Is to {{Homemaker}}?},
  journal = {arXiv preprint arXiv:1607.06520},
  author = {Bolukbasi, Tolga and Chang, Kai-Wei and Zou, James and Saligrama, Venkatesh and Kalai, Adam},
  year = {2016}
}

@book{heise_expressive_2007,
  title = {Expressive {{Order}}},
  publisher = {{Springer}},
  author = {Heise, David R.},
  year = {2007}
}

@article{caliskan_semantics_2017,
  title = {Semantics Derived Automatically from Language Corpora Contain Human-like Biases},
  volume = {356},
  number = {6334},
  journal = {Science},
  author = {Caliskan, Aylin and Bryson, Joanna J. and Narayanan, Arvind},
  year = {2017},
  pages = {183--186}
}

@article{gaddis_how_2017,
  title = {How {{Black Are Lakisha}} and {{Jamal}}? {{Racial Perceptions}} from {{Names Used}} in {{Correspondence Audit Studies}}},
  volume = {4},
  shorttitle = {How {{Black Are Lakisha}} and {{Jamal}}?},
  abstract = {Online correspondence audit studies have emerged as the primary method to examine racial discrimination. Although audits use distinctive names to signal race, few studies scientifically examine data regarding the perception of race from names. Different names treated as black or white may be perceived in heterogeneous ways. I conduct a survey experiment that asks respondents to identify the race they associate with a series of names. I alter the first names given to each respondent and inclusion of last names. Names more commonly given by highly educated black mothers (e.g., Jalen and Nia) are less likely to be perceived as black than names given by less educated black mothers (e.g., DaShawn and Tanisha). The results suggest that a large body of social science evidence on racial discrimination operates under a misguided assumption that all black names are alike, and the findings from correspondence audits are likely sensitive to name selection.},
  journal = {Sociological Science},
  author = {Gaddis, S. Michael},
  month = sep,
  year = {2017},
  pages = {469-489}
}


@article{schroder_affective_2013,
  title = {The Affective Meanings of Automatic Social Behaviors: {{Three}} Mechanisms That Explain Priming.},
  volume = {120},
  shorttitle = {The Affective Meanings of Automatic Social Behaviors},
  number = {1},
  journal = {Psychological Review},
  author = {Schr\"oder, Tobias and Thagard, Paul},
  year = {2013},
  pages = {255}
}


@article{maclin2001racial,
  title={Racial categorization of faces: The ambiguous race face effect.},
  author={MacLin, Otto H and Malpass, Roy S},
  journal={Psychology, Public Policy, and Law},
  volume={7},
  number={1},
  pages={98},
  year={2001},
  publisher={American Psychological Association}
}
@article{kunda_forming_1996,
  title = {Forming Impressions from Stereotypes, Traits, and Behaviors: {{A}} Parallel-Constraint-Satisfaction Theory},
  volume = {103},
  copyright = {(c) 2012 APA, all rights reserved},
  shorttitle = {Forming Impressions from Stereotypes, Traits, and Behaviors},
  abstract = {The authors propose a parallel-constraint-satisfaction theory of impression formation that assumes that social stereotypes and individuating information such as traits or behaviors constrain each other's meaning and jointly influence impressions of individuals. Building on models of text comprehension (W. Kintsch, see record 1988-28529-001), the authors describe a connectionist model that can account for the major findings on how stereotypes affect impressions of individuals in the presence of different kinds of individuating information; how stereotypes, behaviors, and traits affect each other's meaning; and how multiple stereotypes jointly affect impressions. Most of these findings can be modeled by constraint networks, which suggests that they may be due to relatively automatic processes that require little conscious inference. The authors also point to a small number of phenomena that involve more controlled processes. The advantages of the authors' parallel model over serial models are discussed.},
  number = {2},
  journal = {Psychological Review},
  author = {Kunda, Ziva and Thagard, Paul},
  year = {1996},
  keywords = {*Computer Simulation,*Impression Formation,*Personality Traits,Stereotyped Attitudes},
  pages = {284-308}
}
@book{sterling2009art,
  title={The art of agent-oriented modeling},
  author={Sterling, Leon and Taveter, Kuldar},
  year={2009},
  publisher={MIT press}
}
@article{penner_engendering_2013,
  title = {Engendering {{Racial Perceptions An Intersectional Analysis}} of {{How Social Status Shapes Race}}},
  volume = {27},
  abstract = {Intersectionality emphasizes that race, class, and gender distinctions are inextricably intertwined, but fully interrogating the co-constitution of these axes of stratification has proven difficult to implement in large-scale quantitative analyses. We address this gap by exploring gender differences in how social status shapes race in the United States. Building on previous research showing that changes in the racial classifications of others are influenced by social status, we use longitudinal data to examine how differences in social class position might affect racial classification differently for women and men. In doing so, we provide further support for the claim that race, class, and gender are not independent axes of stratification; rather they intersect, creating dynamic feedback loops that maintain the complex structure of social inequality in the United States.},
  language = {en},
  number = {3},
  journal = {Gender \& Society},
  author = {Penner, Andrew M. and Saperstein, Aliya},
  month = jun,
  year = {2013},
  keywords = {Race,gender,incarceration,poverty,stratification,unemployment,welfare},
  pages = {319-344}
}


@misc{robyn_speer_2018_1443582,
  author       = {Robyn Speer and
                  Joshua Chin and
                  Andrew Lin and
                  Sara Jewett and
                  Lance Nathan},
  title        = {LuminosoInsight/wordfreq: v2.2},
  month        = oct,
  year         = 2018,
  doi          = {10.5281/zenodo.1443582},
  url          = {https://doi.org/10.5281/zenodo.1443582}
}
@article{davenport_role_2016,
  title = {The {{Role}} of {{Gender}}, {{Class}}, and {{Religion}} in {{Biracial Americans}}' {{Racial Labeling Decisions}}},
  volume = {81},
  number = {1},
  journal = {American Journal of Sociology},
  author = {Davenport, Lauren D.},
  year = {2016},
  pages = {57-84}
}



@article{crenshaw_demarginalizing_1989,
  title = {Demarginalizing the Intersection of Race and Sex: {{A}} Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics},
  shorttitle = {Demarginalizing the Intersection of Race and Sex},
  journal = {U. Chi. Legal F.},
  author = {Crenshaw, Kimberle},
  year = {1989},
  pages = {139}
}



@article{ehret_modeling_2014,
  title = {Modeling the {{Dynamics}} of {{Evaluation A Multilevel Neural Network Implementation}} of the {{Iterative Reprocessing Model}}},
  abstract = {We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory.},
  language = {en},
  journal = {Personality and Social Psychology Review},
  author = {Ehret, Phillip J. and Monroe, Brian M. and Read, Stephen J.},
  month = aug,
  year = {2014},
  keywords = {Computational modeling,attitudes,automatic/implicit processes,evaluation,neural networks,person perception,social neuroscience,social cognition},
  pages = {1088868314544221},
  pmid = {25168638}
}

@article{robinson2016mean,
  title={Mean Affective Ratings of 932 Identities, 810 Behaviors, and 660 Modifiers by University of Georgia Undergraduates in 2012--2014},
  author={Robinson, Dawn T and Smith-Lovin, Lynn},
  journal={Distributed at UGA Affect Control Theory Website},
  year={2016}
}
@article{dow_deadly_2016,
  title = {The Deadly Challenges of Raising {{African American}} Boys: {{Navigating}} the Controlling Image of the ``Thug''},
  volume = {30},
  shorttitle = {The Deadly Challenges of Raising {{African American}} Boys},
  number = {2},
  journal = {Gender \& Society},
  author = {Dow, Dawn Marie},
  year = {2016},
  pages = {161--188}
}




@article{robinson_control_2007,
  title = {Control Theories in Sociology},
  volume = {33},
  number = {1},
  journal = {Annual Review of Sociology},
  author = {Robinson, Dawn T.},
  year = {2007},
  pages = {157}
}

@article{romney_culture_1986,
  title = {Culture as Consensus: {{A}} Theory of Culture and Informant Accuracy},
  volume = {88},
  shorttitle = {Culture as Consensus},
  number = {2},
  journal = {American anthropologist},
  author = {Romney, A. Kimball and Weller, Susan C. and Batchelder, William H.},
  year = {1986},
  pages = {313--338}
}

@article{paszke_automatic_2017,
  title = {Automatic Differentiation in Pytorch},
  author = {Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year = {2017}
}

@article{todorov_social_2015,
  title = {Social {{Attributions}} from {{Faces}}: {{Determinants}}, {{Consequences}}, {{Accuracy}}, and {{Functional Significance}}},
  volume = {66},
  shorttitle = {Social {{Attributions}} from {{Faces}}},
  abstract = {Since the early twentieth century, psychologists have known that there is consensus in attributing social and personality characteristics from facial appearance. Recent studies have shown that surprisingly little time and effort are needed to arrive at this consensus. Here we review recent research on social attributions from faces. Section I outlines data-driven methods capable of identifying the perceptual basis of consensus in social attributions from faces (e.g., What makes a face look threatening?). Section II describes nonperceptual determinants of social attributions (e.g., person knowledge and incidental associations). Section III discusses evidence that attributions from faces predict important social outcomes in diverse domains (e.g., investment decisions and leader selection). In Section IV, we argue that the diagnostic validity of these attributions has been greatly overstated in the literature. In the final section, we offer an account of the functional significance of these attributions.},
  number = {1},
  journal = {Annual Review of Psychology},
  author = {Todorov, Alexander and Olivola, Christopher Y. and Dotsch, Ron and {Mende-Siedlecki}, Peter},
  year = {2015},
  pages = {519-545},
  pmid = {25196277}
}




@inproceedings{de-arteaga_bias_2019,
  title={Bias in bios: A case study of semantic representation bias in a high-stakes setting},
  author={De-Arteaga, Maria and Romanov, Alexey and Wallach, Hanna and Chayes, Jennifer and Borgs, Christian and Chouldechova, Alexandra and Geyik, Sahin and Kenthapadi, Krishnaram and Kalai, Adam Tauman},
  booktitle={Proceedings of the Conference on Fairness, Accountability, and Transparency},
  pages={120--128},
  year={2019}
}



@inproceedings{joseph_exploring_2016,
  title = {Exploring Patterns of Identity Usage in Tweets: A New Problem, Solution and Case Study},
  shorttitle = {Exploring Patterns of Identity Usage in Tweets},
  booktitle = {Proceedings of the 25th {{International Conference}} on {{World Wide Web}}},
  author = {Joseph, Kenneth and Wei, Wei and Carley, Kathleen M.},
  year = {2016},
  pages = {401--412}
}



@incollection{RobinsonAffectControlTheory2006,
  series = {Handbooks of {{Sociology}} and {{Social Research}}},
  title = {Affect {{Control Theory}}},
  copyright = {\textcopyright{}2006 Springer Science+Business Media, LLC},
  isbn = {978-0-387-30713-8 978-0-387-30715-2},
  abstract = {When David Heise (1977,1979) published his early statements of affect control theory, contribut-ing to the newly developing sociology of emotion was not his primary goal. The main objective of the theory was to explain behavior in the context of social interactions. Heise hoped to develop a formal framework that could describe both the routine, expected role behaviors that people enact under normal circumstances and the creative responses they generate when encountering noninstitutionalized or counternormative situations. He combined insights from a measurement tradition in psycholinguistics (Osgood 1962, 1966; Osgood et al. 1973, 1975), empirical studies of impression formation (Gollob 1968; Gollob and Rossman 1973; Heise 1969, 1970), and a cybernetic model of perception (Powers 1973) to create his new theory of social action.},
  language = {en},
  booktitle = {Handbook of the {{Sociology}} of {{Emotions}}},
  author = {Robinson, Dawn T. and {Smith-Lovin}, Lynn and Wisecup, Allison K.},
  month = jan,
  year = {2006},
  keywords = {Sociology,Personality \& Social Psychology},
  pages = {179-202}
}



@inproceedings{joseph_girls_2017,
  title = {Girls Rule, Boys Drool: {{Extracting}} Semantic and Affective Stereotypes from {{Twitter}}},
  booktitle = {2017 {{ACM Conference}} on {{Computer Supported Cooperative Work}}.({{CSCW}})},
  author = {Joseph, Kenneth and Wei, Wei and Carley, Kathleen M.},
  year = {2017}
}




@book{heise_understanding_1979,
  title = {Understanding Events: {{Affect}} and the Construction of Social Action},
  shorttitle = {Understanding Events},
  publisher = {{CUP Archive}},
  author = {Heise, David R.},
  year = {1979}
}



@article{karabatsos_markov_2003,
  title = {Markov Chain Estimation for Test Theory without an Answer Key},
  volume = {68},
  number = {3},
  journal = {Psychometrika},
  author = {Karabatsos, George and Batchelder, William H.},
  year = {2003},
  pages = {373--389}
}

@article{batchelder_test_1988,
  title = {Test Theory without an Answer Key},
  volume = {53},
  number = {1},
  journal = {Psychometrika},
  author = {Batchelder, William H. and Romney, A. Kimball},
  year = {1988},
  pages = {71--92}
}



@book{smith-lovin_affect_1988,
  title = {Affect Control Theory: {{Research}} Advances},
  shorttitle = {Affect Control Theory},
  publisher = {{New York: Gordon and Breach}},
  author = {{Smith-Lovin}, Lynn and Heise, David R.},
  year = {1988}
}

@article{rogers_you_2018,
  title = {Do {{You See What I See}}? {{Testing}} for {{Individual Differences}} in {{Impressions}} of {{Events}}},
  volume = {81},
  shorttitle = {Do {{You See What I See}}?},
  number = {2},
  journal = {Social Psychology Quarterly},
  author = {Rogers, Kimberly B.},
  year = {2018},
  pages = {149--172}
}





@article{hoff_latent_2002,
  title = {Latent Space Approaches to Social Network Analysis},
  volume = {97},
  number = {460},
  journal = {Journal of the american Statistical association},
  author = {Hoff, Peter D. and Raftery, Adrian E. and Handcock, Mark S.},
  year = {2002},
  pages = {1090--1098}
}





@article{collins_spreading-activation_1975,
  title = {A Spreading-Activation Theory of Semantic Processing.},
  volume = {82},
  number = {6},
  journal = {Psychological review},
  author = {Collins, Allan M. and Loftus, Elizabeth F.},
  year = {1975},
  pages = {407}
}


@article{KatzRacialstereotypesone1933,
  title = {Racial Stereotypes of One Hundred College Students},
  volume = {28},
  copyright = {(c) 2012 APA, all rights reserved},
  abstract = {The degree of agreement among the students in assigning characteristics from a list of 84 adjectives to different races seemed too great to be the result solely of the students' contacts with members of those races. Individual experience may have entered into a student's judgment, but it probably did so to confirm the original stereotype which he had learned. Because human beings from time to time exhibit all kinds of behavior he could find confirmation of his views. By omitting cases which contradict the stereotype, the individual becomes convinced from association with a race that its members are just the kind of people he always thought they were. The manner in which public and private attitudes are bound up together was shown in the order of the 10 racial and national groups as determined by the definiteness with which students assigned characteristics to them. The definiteness of the stereotyped picture of a race, however, had little relation to the prejudice exhibited against that race.},
  number = {3},
  journal = {The Journal of Abnormal and Social Psychology},
  author = {Katz, D. and Braly, K.},
  year = {1933},
  keywords = {college students,racial groups,racial stereotypes,students judgment},
  pages = {280-290}
}

@book{sherif_common_1966,
  title = {In Common Predicament: {{Social}} Psychology of Intergroup Conflict and Cooperation},
  shorttitle = {In Common Predicament},
  publisher = {{Houghton Mifflin Boston}},
  author = {Sherif, Muzafer},
  year = {1966}
}

@book{thomas_child_1928,
  title = {The {{Child}} in {{America}}},
  shorttitle = {The {{Child}} in {{America}}. {{New York}}},
  publisher = {{Knopf}},
  author = {Thomas, William I. and Thomas, Dorothy S.},
  year = {1928}
}

@article{allport_nature_1954,
  title = {The Nature of Prejudice},
  author = {Allport, Gordon Willard and Clark, Kenneth and Pettigrew, Thomas},
  year = {1954}
}




@article{morgan_distinguishing_2016,
  title = {Distinguishing {{Normative Processes}} from {{Noise}}: {{A Comparison}} of {{Four Approaches}} to {{Modeling Impressions}} of {{Social Events}}},
  volume = {79},
  shorttitle = {Distinguishing {{Normative Processes}} from {{Noise}}},
  number = {4},
  journal = {Social Psychology Quarterly},
  author = {Morgan, Jonathan H. and Rogers, Kimberly B. and Hu, Mao},
  year = {2016},
  pages = {311--332}
}




@book{heise_surveying_2010,
  title = {Surveying Cultures: {{Discovering}} Shared Conceptions and Sentiments},
  shorttitle = {Surveying Cultures},
  publisher = {{John Wiley \& Sons}},
  author = {Heise, David R.},
  year = {2010}
}



@book{thomas_child_1928,
  title = {The {{Child}} in {{America}}},
  shorttitle = {The {{Child}} in {{America}}. {{New York}}},
  publisher = {{Knopf}},
  author = {Thomas, William I. and Thomas, Dorothy S.},
  year = {1928}
}

@book{enos_space_2017,
  title = {The Space between Us: {{Social}} Geography and Politics},
  shorttitle = {The Space between Us},
  publisher = {{Cambridge University Press}},
  author = {Enos, Ryan D.},
  year = {2017}
}

@phdthesis{morgan_duality_2018,
  title = {The {{Duality}} of {{Identities}} and {{Groups}}: {{The Effects}} of {{Status Homophily}} on {{Social Interactions}} and {{Relations}}},
  school = {Duke University},
  author = {Morgan, Jonathan H.},
  year = {2018}
}

@book{ridgeway_framed_2011,
  title = {Framed by Gender: {{How}} Gender Inequality Persists in the Modern World},
  shorttitle = {Framed by Gender},
  publisher = {{Oxford University Press}},
  author = {Ridgeway, Cecilia L.},
  year = {2011}
}


@article{kozlowski_geometry_2018,
  title = {The {{Geometry}} of {{Culture}}: {{Analyzing}} the {{Meanings}} of {{Class}} through {{Word Embeddings}}},
  volume = {84},
  shorttitle = {The {{Geometry}} of {{Culture}}},
  abstract = {We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences (rich \textendash{} poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.},
  language = {en},
  number = {5},
  journal = {American Sociological Review},
  author = {Kozlowski, Austin C. and Taddy, Matt and Evans, James A.},
  month = oct,
  year = {2019},
  pages = {905-949}
}




@article{berger_status_1992,
  title = {Status Inconsistency in Task Situations: {{A}} Test of Four Status Processing Principles},
  shorttitle = {Status Inconsistency in Task Situations},
  journal = {American Sociological Review},
  author = {Berger, Joseph and Norman, Robert Z. and Balkwell, James W. and Smith, Roy F.},
  year = {1992},
  pages = {843--855}
}

@article{mccall_identities_1978,
  title = {Identities and Interactions},
  journal = {New York},
  author = {McCall, George J. and Simmons, Jerry L.},
  year = {1978}
}

@techreport{winter_simplified_2019,
  address = {Rochester, NY},
  type = {{{SSRN Scholarly Paper}}},
  title = {A {{Simplified Protocol}} to {{Screen Out VPS}} and {{International Respondents Using Qualtrics}}},
  abstract = {This protocol is a much simplified update of our original protocol for screening out international and VPS respondents from Qualtrics surveys. It utilizes Qualtrics' built-in Web Service functionality to interact with IP Hub in looking up potentially problematic respondents.},
  language = {en},
  number = {ID 3327274},
  institution = {{Social Science Research Network}},
  author = {Winter, Nicholas and Burleigh, Tyler and Kennedy, Ryan and Clifford, Scott},
  month = feb,
  year = {2019},
  keywords = {MTurk,Qualtrics,survey research,VPS}
}

@article{burges_ranknet_2010,
  title = {From Ranknet to Lambdarank to Lambdamart: {{An}} Overview},
  volume = {11},
  shorttitle = {From Ranknet to Lambdarank to Lambdamart},
  number = {23-581},
  journal = {Learning},
  author = {Burges, Christopher JC},
  year = {2010},
  pages = {81}
}

@inproceedings{burges_learning_2005,
  title = {Learning to Rank Using Gradient Descent},
  booktitle = {Proceedings of the 22nd {{International Conference}} on {{Machine}} Learning ({{ICML}}-05)},
  author = {Burges, Christopher and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Gregory N.},
  year = {2005},
  pages = {89--96}
}

@article{berger_status_1972,
  title = {Status Characteristics and Social Interaction},
  journal = {American Sociological Review},
  author = {Berger, Joseph and Cohen, Bernard P. and Zelditch Jr, Morris},
  year = {1972},
  pages = {241--255}
}

@article{ridgeway_gender_1999,
  title = {The Gender System and Interaction},
  volume = {25},
  number = {1},
  journal = {Annual review of sociology},
  author = {Ridgeway, Cecilia L. and {Smith-Lovin}, Lynn},
  year = {1999},
  pages = {191--216}
}

@article{rauthmann_situational_2014,
  title = {The {{Situational Eight DIAMONDS}}: {{A}} Taxonomy of Major Dimensions of Situation Characteristics.},
  volume = {107},
  shorttitle = {The {{Situational Eight DIAMONDS}}},
  number = {4},
  journal = {Journal of Personality and Social Psychology},
  author = {Rauthmann, John F. and {Gallardo-Pujol}, David and Guillaume, Esther M. and Todd, Elysia and Nave, Christopher S. and Sherman, Ryne A. and Ziegler, Matthias and Jones, Ashley Bell and Funder, David C.},
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