% 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}
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{Ekman1992AnAF,
  title={An argument for basic emotions},
  author={Paul Ekman},
  journal={Cognition \& Emotion},
  year={1992},
  volume={6},
  pages={169-200},
  url={https://api.semanticscholar.org/CorpusID:11771973}
}

@inproceedings{mohammad-2018-obtaining:,
    title = "Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 {E}nglish Words",
    author = "Mohammad, Saif",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    year = "2018",
}

@misc{poria2019meld,
      title={MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations}, 
      author={Soujanya Poria and Devamanyu Hazarika and Navonil Majumder and Gautam Naik and Erik Cambria and Rada Mihalcea},
      year={2019},
      eprint={1810.02508},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{chen2018emotionlines,
      title={EmotionLines: An Emotion Corpus of Multi-Party Conversations}, 
      author={Sheng-Yeh Chen and Chao-Chun Hsu and Chuan-Chun Kuo and Ting-Hao and Huang and Lun-Wei Ku},
      year={2018},
      eprint={1802.08379},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Bedi_2023,
   title={Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations},
   volume={14},
   ISSN={2371-9850},
   url={http://dx.doi.org/10.1109/TAFFC.2021.3083522},
   DOI={10.1109/taffc.2021.3083522},
   number={2},
   journal={IEEE Transactions on Affective Computing},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Bedi, Manjot and Kumar, Shivani and Akhtar, Md Shad and Chakraborty, Tanmoy},
   year={2023},
   month=apr, pages={1363–1375} }

@misc{song2020mpnet,
      title={MPNet: Masked and Permuted Pre-training for Language Understanding}, 
      author={Kaitao Song and Xu Tan and Tao Qin and Jianfeng Lu and Tie-Yan Liu},
      year={2020},
      eprint={2004.09297},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}



@article{poria2018meld,
  title={Meld: A multimodal multi-party dataset for emotion recognition in conversations},
  author={Poria, Soujanya and Hazarika, Devamanyu and Majumder, Navonil and Naik, Gautam and Cambria, Erik and Mihalcea, Rada},
  journal={arXiv preprint arXiv:1810.02508},
  year={2018}
}



@article{kumar2022discovering,
  title={Discovering emotion and reasoning its flip in multi-party conversations using masked memory network and transformer},
  author={Kumar, Shivani and Shrimal, Anubhav and Akhtar, Md Shad and Chakraborty, Tanmoy},
  journal={Knowledge-Based Systems},
  volume={240},
  pages={108112},
  year={2022},
  publisher={Elsevier}
}


@inproceedings{christ2023muse,
  title={The muse 2023 multimodal sentiment analysis challenge: Mimicked emotions, cross-cultural humour, and personalisation},
  author={Christ, Lukas and Amiriparian, Shahin and Baird, Alice and Kathan, Alexander and M{\"u}ller, Niklas and Klug, Steffen and Gagne, Chris and Tzirakis, Panagiotis and Stappen, Lukas and Me{\ss}ner, Eva-Maria and others},
  booktitle={Proceedings of the 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation},
  pages={1--10},
  year={2023}
}



@article{gupta2023counterspeeches,
  title={Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation},
  author={Gupta, Rishabh and Desai, Shaily and Goel, Manvi and Bandhakavi, Anil and Chakraborty, Tanmoy and Akhtar, Md Shad},
  journal={arXiv preprint arXiv:2305.13776},
  year={2023}
}



@article{sengupta2022comprehensive,
  title={A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer},
  author={Sengupta, Ayan and Suresh, Tharun and Akhtar, Md Shad and Chakraborty, Tanmoy},
  journal={arXiv preprint arXiv:2204.12753},
  year={2022}
}


@article{kumar2023emotion,
  title={Emotion Flip Reasoning in Multiparty Conversations},
  author={Kumar, Shivani and Dudeja, Shubham and Akhtar, Md Shad and Chakraborty, Tanmoy},
  journal={arXiv preprint arXiv:2306.13959},
  year={2023}
}


@article{bedi2021multi,
  title={Multi-modal sarcasm detection and humor classification in code-mixed conversations},
  author={Bedi, Manjot and Kumar, Shivani and Akhtar, Md Shad and Chakraborty, Tanmoy},
  journal={IEEE Transactions on Affective Computing},
  year={2021},
  publisher={IEEE}
}




@inproceedings{kumar2023explaining,
  title={Explaining (sarcastic) utterances to enhance affect understanding in multimodal dialogues},
  author={Kumar, Shivani and Mondal, Ishani and Akhtar, Md Shad and Chakraborty, Tanmoy},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
 
  pages={12986--12994},
  year={2023}
}


@inproceedings{desai2022nice,
   abstract = {Sarcasm is a pervading linguistic phenomenon and highly challenging to explain due to its subjectivity, lack of context and deeply-felt opinion. In the multimodal setup, sarcasm is conveyed through the incongruity between the text and visual entities. Although recent approaches deal with sarcasm as a classification problem, it is unclear why an online post is identified as sarcastic. Without proper explanation, end users may not be able to perceive the underlying sense of irony. In this paper, we propose a novel problem - Multimodal Sarcasm Explanation (MuSE) - given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm. To this end, we develop MORE, a new dataset with explanation of 3510 sarcastic multimodal posts. Each explanation is a natural language (English) sentence describing the hidden irony. We benchmark MORE by employing a multimodal Transformer-based architecture. It incorporates a cross-modal attention in the Transformer's encoder which attends to the distinguishing features between the two modalities. Subsequently, a BART-based auto-regressive decoder is used as the generator. Empirical results demonstrate convincing results over various baselines (adopted for MuSE) across five evaluation metrics. We also conduct human evaluation on predictions and obtain Fleiss' Kappa score of 0:4 as a fair agreement among 25 evaluators.},
   author = {Poorav Desai and Tanmoy Chakraborty and Md Shad Akhtar},
   doi = {10.1609/aaai.v36i10.21300},
   issn = {2159-5399},
   journal = {Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022},
   title = {Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation},
   volume = {36},
   year = {2022},
}

@article{kumar2022did,
  title={When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues},
  author={Kumar, Shivani and Kulkarni, Atharva and Akhtar, Md Shad and Chakraborty, Tanmoy},
  journal={arXiv preprint arXiv:2203.06419},
  year={2022}
}

@article{xu2023large,
  title={Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation From Deductive, Inductive and Abductive Views},
  author={Xu, Fangzhi and Lin, Qika and Han, Jiawei and Zhao, Tianzhe and Liu, Jun and Cambria, Erik},
  journal={arXiv preprint arXiv:2306.09841},
  year={2023}
}

@misc{roy2022machine,
  title={Machine learning techniques for word-based text similarity determinations},
  author={Roy, Suman and Kumar, Amit and Bhattacharjee, Sourabh Kumar and Kumar, Shashi and Paka, William Scott and Chakraborty, Tanmoy},
  year={2022},
  month=dec # "~29",
  publisher={Google Patents},
  note={US Patent App. 17/355,731}
}
@inproceedings{yadav2021twitter,
  title={Twitter sentiment analysis using supervised machine learning},
  author={Yadav, Nikhil and Kudale, Omkar and Rao, Aditi and Gupta, Srishti and Shitole, Ajitkumar},
  booktitle={Intelligent data communication technologies and internet of things: Proceedings of ICICI 2020},
  pages={631--642},
  year={2021},
  organization={Springer}
}
@inproceedings{moreno2020assembly,
  title={Assembly of Polarity, Emotion and User Statistics for Detection of Fake Profiles.},
  author={Moreno-Sandoval, Luis Gabriel and Alvarado-Valencia, Jorge Andres},
booktitle={-},
  journal={-},
  year={2020}
}




@inproceedings{kumar-etal-2023-multilingual,
    title = "From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues",
    author = "Kumar, Shivani  and
      S, Ramaneswaran  and
      Akhtar, Md  and
      Chakraborty, Tanmoy",
    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.598",
    doi = "10.18653/v1/2023.emnlp-main.598",
    pages = "9638--9652",
    abstract = "Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in code-mixed conversations has received relatively less attention. This motivates our undertaking of ERC for code-mixed conversations in this study. Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input. Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue!
  representation obtained from a dedicated dialogue understanding module. Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC. Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.",
}





\section{Introduction}
@article{cuadrado2023team,
  title={team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach},
  author={Cuadrado, Juan and Martinez, Elizabeth and Martinez-Santos, Juan Carlos and Puertas, Edwin},
  journal={-},
  year={2023},
  publisher={Cartagena de Indias}
}
@INPROCEEDINGS{9989700,
  author={Puertas, Edwin and Martinez-Santos, Juan Carlos and Andrés Pertuz-Duran, Pablo},
  booktitle={2022 IEEE ANDESCON}, 
  title={Presidential preferences in Colombia through Sentiment Analysis}, 
  year={2022},
  volume={},
  number={},
  pages={1-5},
  keywords={Analytical models;Sentiment analysis;Social networking (online);Natural languages;Blogs;Bit error rate;Machine learning;natural language processing;sentimental analysis;presidential debate;language model},
  doi={10.1109/ANDESCON56260.2022.9989700}}

@article{martinez2023automated,
  title={Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model},
  author={Martinez, Elizabeth and Cuadrado, Juan and Martinez-Santos, Juan Carlos and Pe{\~n}a, Daniel and Puertas, Edwin},
  journal={-},
  year={2023},
  publisher={Cartagena de Indias}
}

@inproceedings{cuadrado2023utb,
  title={Utb-nlp at semeval-2023 task 3: Weirdness, lexical features for detecting categorical framings, and persuasion in online news},
  author={Cuadrado, Juan and Martinez, Elizabeth and Morillo, Anderson and Pe{\~n}a, Daniel and Sossa, Kevin and Martinez-Santos, Juan and Puertas, Edwin},
  booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={1551--1557},
  year={2023}
}

@article{moreno2019celebrity,
  title={Celebrity profiling on twitter using sociolinguistic},
  author={Moreno-Sandoval, Luis Gabriel and Puertas, Edwin and Plaza-del-Arco, Flor Miriam and Pomares-Quimbaya, Alexandra and Alvarado-Valencia, Jorge Andres and Alfonso, L},
  journal={CLEF (Working Notes)},
  year={2019}
}

@article{puertas2021detection,
  title={Detection of sociolinguistic features in digital social networks for the detection of communities},
  author={Puertas, Edwin and Moreno-Sandoval, Luis Gabriel and Redondo, Javier and Alvarado-Valencia, Jorge Andres and Pomares-Quimbaya, Alexandra},
  journal={Cognitive Computation},
  volume={13},
  pages={518--537},
  year={2021},
  publisher={Springer}
}

@article{puertas2019bots,
  title={Bots and gender profiling on twitter using sociolinguistic features},
  author={Puertas, Edwin and Moreno-Sandoval, Luis Gabriel and Plaza-del Arco, Flor Miriam and Alvarado-Valencia, Jorge Andres and Pomares-Quimbaya, Alexandra and Alfonso, L},
  journal={CLEF (Working Notes)},
  pages={1--8},
  year={2019}
}


@article{puertas2021phonetic,
  title={Phonetic detection for hate speech spreaders on Twitter},
  author={Puertas, Edwin and Martinez-Santos, Juan Carlos},
  journal={-},
  year={2021},
  publisher={Cartagena de Indias}
}



@article{marrugo2023natural,
  title={Natural language content evaluation system for multiclass detection of hate speech in tweets using transformers},
  author={Marrugo-Tob{\'o}n, Duv{\'a}n Andres and Martinez-Santos, Juan Carlos and Puertas, Edwin},
  year={2023},
  publisher={Cartagena de Indias}
}



@ARTICLE{wang2022multimodal,
  author={Wang, Fanfan and Ding, Zixiang and Xia, Rui and Li, Zhaoyu and Yu, Jianfei},
  journal={IEEE Transactions on Affective Computing}, 
  title={Multimodal Emotion-Cause Pair Extraction in Conversations}, 
  year={2022},
  pages={1-12},
  issn = {1949-3045},
  doi = {10.1109/TAFFC.2022.3226559}
}

@article{KUMAR2022108112,

title = {Discovering emotion and reasoning its flip in multi-party conversations using masked memory network and transformer},

journal = {Knowledge-Based Systems},

volume = {240},

pages = {108112},

year = {2022},

issn = {0950-7051},

doi = {https://doi.org/10.1016/j.knosys.2021.108112},

url = {https://www.sciencedirect.com/science/article/pii/S0950705121011709},

author = {Shivani Kumar and Anubhav Shrimal and Md Shad Akhtar and Tanmoy Chakraborty},

keywords = {Emotion recognition, Emotion-Flip Reasoning, Multi-party conversations},

abstract = {Efficient discovery of a speaker's emotional states in a multi-party conversation is significant to design human-like conversational agents. During a conversation, the cognitive state of a speaker often alters due to certain past utterances, which may lead to a flip in their emotional state. Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances. In this paper, along with addressing the task of emotion recognition in conversations (ERC), we introduce a novel task – Emotion-Flip Reasoning (EFR), that aims to identify past utterances which have triggered one's emotional state to flip at a certain time. We propose a masked memory network to address the former and a Transformer-based network for the latter task. To this end, we consider MELD, a benchmark emotion recognition dataset in multi-party conversations for the task of ERC, and augment it wi!
 th new ground-truth labels for EFR. An extensive comparison with five state-of-the-art models suggests improved performances of our models for both the tasks. We further present anecdotal evidence and both qualitative and quantitative error analyses to support the superiority of our models compared to the baselines.}

}
@ARTICLE{10164178,

  author={Kumar, Shivani and Dudeja, Shubham and Akhtar, Md Shad and Chakraborty, Tanmoy},

  journal={IEEE Transactions on Artificial Intelligence}, 

  title={Emotion Flip Reasoning in Multiparty Conversations}, 

  year={2024},

  volume={5},

  number={3},

  pages={1339-1348},

  keywords={Emotion recognition;Task analysis;Cognition;Electronic mail;Psychology;Information technology;Transformers;Conversational dialogues;emotion flip reasoning (EFR);emotion reasoning;instigators},

  doi={10.1109/TAI.2023.3289937}}

@inproceedings{kumar2024semeval,

    title = "SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)",

    author = "Kumar, Shivani  and

      Akhtar, Md Shad  and

      Cambria, Erik and

      Chakraborty, Tanmoy",

    booktitle = "Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics",

    year = "2024",

    publisher = "Association for Computational Linguistics",

    url = "https://arxiv.org/abs/2402.18944",

}