% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

% Begin new references

@article{makcedward2019,
    author    = {Edward Ma},
    title     = {Data Augmentation in NLP: Introduction to Text Augmentation},
    journal   = {Towards Data Science},
    year      = {2019},
    url       = {https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28},
    note    = {Repository: makcedward/nlpaug}
}

@misc{bhargava2021generalization,
      title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, 
      author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
      year={2021},
      eprint={2110.01518},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-1810-04805,
  author       = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},
  title        = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},
  journal      = {CoRR},
  volume       = {abs/1810.04805},
  year         = {2018},
  url          = {http://arxiv.org/abs/1810.04805},
  eprinttype   = {arXiv},
  eprint       = {1810.04805},
  timestamp    = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1908-08962,
  author    = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},
  title     = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation},
  journal   = {CoRR},
  volume    = {abs/1908.08962},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.08962},
  eprinttype = {arXiv},
  eprint    = {1908.08962},
  timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{wei2023chainofthought,
      title={Chain-of-Thought Prompting Elicits Reasoning in Large Language Models}, 
      author={Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
      year={2023},
      eprint={2201.11903},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-1811-00937,
  author       = {Alon Talmor and
                  Jonathan Herzig and
                  Nicholas Lourie and
                  Jonathan Berant},
  title        = {CommonsenseQA: {A} Question Answering Challenge Targeting Commonsense
                  Knowledge},
  journal      = {CoRR},
  volume       = {abs/1811.00937},
  year         = {2018},
  url          = {http://arxiv.org/abs/1811.00937},
  eprinttype    = {arXiv},
  eprint       = {1811.00937},
  timestamp    = {Thu, 22 Nov 2018 17:58:30 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1811-00937.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{hirschman_gaizauskas_2001, 
    title={Natural language question answering: the view from here}, volume={7}, 
    DOI={10.1017/S1351324901002807}, 
    number={4}, 
    journal={Natural Language Engineering}, 
    publisher={Cambridge University Press}, 
    author={Hirschman, L. and Gaizauskas, R.}, 
    year={2001}, 
    pages={275–300}
}
@article{laurer_less_2023,
    title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
    issn = {1047-1987, 1476-4989},
    shorttitle = {Less {Annotating}, {More} {Classifying}},
    url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
    doi = {10.1017/pan.2023.20},
    language = {en},
    urldate = {2023-06-20},
    journal = {Political Analysis},
    author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
    month = jun,
    year = {2023},
    pages = {1--33},
}
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{liu2019roberta,
      title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, 
      author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
      year={2019},
      eprint={1907.11692},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{bender-koller-2020-climbing,
    title = "Climbing towards {NLU}: {On} Meaning, Form, and Understanding in the Age of Data",
    author = "Bender, Emily M.  and
      Koller, Alexander",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.463",
    doi = "10.18653/v1/2020.acl-main.463",
    pages = "5185--5198",
    abstract = "The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as {``}understanding{''} language or capturing {``}meaning{''}. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of {``}Taking Stock of Where We{'}ve Been and Where We{'}re Going{''}, we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.",
}

@misc{geva2021did,
      title={Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies}, 
      author={Mor Geva and Daniel Khashabi and Elad Segal and Tushar Khot and Dan Roth and Jonathan Berant},
      year={2021},
      eprint={2101.02235},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{bosselut-etal-2019-comet,
    title = "{COMET}: Commonsense Transformers for Automatic Knowledge Graph Construction",
    author = "Bosselut, Antoine  and
      Rashkin, Hannah  and
      Sap, Maarten  and
      Malaviya, Chaitanya  and
      Celikyilmaz, Asli  and
      Choi, Yejin",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    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://aclanthology.org/P19-1470",
    doi = "10.18653/v1/P19-1470",
    pages = "4762--4779",
    abstract = "We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge. We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and propose COMmonsEnse Transformers (COMET) that learn to generate rich and diverse commonsense descriptions in natural language. Despite the challenges of commonsense modeling, our investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs. Empirical results demonstrate that COMET is able to generate novel knowledge that humans rate as high quality, with up to 77.5{\%} (ATOMIC) and 91.7{\%} (ConceptNet) precision at top 1, which approaches human performance for these resources. Our findings suggest that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.",
}

@misc{li2023search,
      title={In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search}, 
      author={Huihan Li and Yuting Ning and Zeyi Liao and Siyuan Wang and Xiang Lorraine Li and Ximing Lu and Faeze Brahman and Wenting Zhao and Yejin Choi and Xiang Ren},
      year={2023},
      eprint={2311.07237},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{hartmann2023sok,
      title={SoK: Memorization in General-Purpose Large Language Models}, 
      author={Valentin Hartmann and Anshuman Suri and Vincent Bindschaedler and David Evans and Shruti Tople and Robert West},
      year={2023},
      eprint={2310.18362},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{jiang-etal-2023-brainteaser,
    title = "{BRAINTEASER}: Lateral Thinking Puzzles for Large Language Models",
    author = "Jiang, Yifan  and
      Ilievski, Filip  and
      Ma, Kaixin  and
      Sourati, Zhivar",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.885",
    doi = "10.18653/v1/2023.emnlp-main.885",
    pages = "14317--14332",
    abstract = "The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms. While such vertical thinking tasks have been relatively popular, lateral thinking puzzles have received little attention. To bridge this gap, we devise BrainTeaser: a multiple-choice Question Answering task designed to test the model{'}s ability to exhibit lateral thinking and defy default commonsense associations. We design a three-step procedure for creating the first lateral thinking benchmark, consisting of data collection, distractor generation, and generation of adversarial examples, leading to 1,100 puzzles with high-quality annotations. To assess the consistency of lateral reasoning by models, we enrich BrainTeaser based on a semantic and contextual reconstruction of its questions. Our experiments with state-of-the-art instruction- and commonsense language models reveal a significant gap between human and model performance, which is further widened when consistency across adversarial formats is considered. We make all of our code and data available to stimulate work on developing and evaluating lateral thinking models.",
}

@InProceedings{jiang-ilievski-ma:2024:SemEval2024,
  author    = {Jiang, Yifan  and  Ilievski, Filip  and  Ma, Kaixin},
  title     = {SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense},
  booktitle      = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
  month          = {June},
  year           = {2024},
  address        = {Mexico City, Mexico},
  publisher      = {Association for Computational Linguistics},
  pages     = {1996--2010},
  abstract  = {While vertical thinking relies on logical and commonsense reasoning, lateral thinking requires systems to defy commonsense associations and overwrite them through unconventional thinking. Lateral thinking has been shown to be challenging for current models but has received little attention. A recent benchmark, BRAINTEASER, aims to evaluate current models' lateral thinking ability in a zero-shot setting. In this paper, we split the original benchmark to also support fine-tuning setting and present SemEval Task 9, BRAINTEASER(S), the first task at this competition designed to test the system's reasoning and lateral thinking ability. As a popular task, BRAINTEASER(S)'s two subtasks receive 483 team submissions from 182 participants during the competition. This paper provides a fine-grained system analysis of the competition results, together with a reflection on what this means for the ability of the systems to reason laterally. We hope that the BRAINTEASER(S) subtasks and findings in this paper can stimulate future work on lateral thinking and robust reasoning by computational models},
  url       = {https://aclanthology.org/2024.semeval2024-1.271}
}