% 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}
}

@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",

}

@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.",
}

@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}
}

@inproceedings{NIPS2017_3f5ee243,
 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},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Attention is All you Need},
 url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},
 volume = {30},
 year = {2017}
}

@misc{loshchilov2019decoupled,
      title={Decoupled Weight Decay Regularization}, 
      author={Ilya Loshchilov and Frank Hutter},
      year={2019},
      eprint={1711.05101},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{cho2014learning,
      title={Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation}, 
      author={Kyunghyun Cho and Bart van Merrienboer and Caglar Gulcehre and Dzmitry Bahdanau and Fethi Bougares and Holger Schwenk and Yoshua Bengio},
      year={2014},
      eprint={1406.1078},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@misc{chung2014empirical,
      title={Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling}, 
      author={Junyoung Chung and Caglar Gulcehre and KyungHyun Cho and Yoshua Bengio},
      year={2014},
      eprint={1412.3555},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}


@misc{demszky2020goemotions,
      title={GoEmotions: A Dataset of Fine-Grained Emotions}, 
      author={Dorottya Demszky and Dana Movshovitz-Attias and Jeongwoo Ko and Alan Cowen and Gaurav Nemade and Sujith Ravi},
      year={2020},
      eprint={2005.00547},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@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}
}

@misc{zhang2019pegasus,
    title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
    author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
    year={2019},
    eprint={1912.08777},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@inproceedings{Bhat:2014:ISS:2824864.2824872,
author = {Bhat, Irshad Ahmad and Mujadia, Vandan and Tammewar, Aniruddha and Bhat, Riyaz Ahmad and Shrivastava, Manish}, title = {IIIT-H System Submission for FIRE2014 Shared Task on Transliterated Search}, booktitle = {Proceedings of the Forum for Information Retrieval Evaluation}, series = {FIRE '14}, year = {2015}, isbn = {978-1-4503-3755-7}, location = {Bangalore, India}, pages = {48--53}, numpages = {6}, url = {http://doi.acm.org/10.1145/2824864.2824872}, doi = {10.1145/2824864.2824872}, acmid = {2824872}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Information Retrieval, Language Identification, Language Modeling, Perplexity, Transliteration},
}

@misc{communication2023seamlessm4t,
      title={SeamlessM4T: Massively Multilingual & Multimodal Machine Translation}, 
      author={Seamless Communication and Loïc Barrault and Yu-An Chung and Mariano Cora Meglioli and David Dale and Ning Dong and Paul-Ambroise Duquenne and Hady Elsahar and Hongyu Gong and Kevin Heffernan and John Hoffman and Christopher Klaiber and Pengwei Li and Daniel Licht and Jean Maillard and Alice Rakotoarison and Kaushik Ram Sadagopan and Guillaume Wenzek and Ethan Ye and Bapi Akula and Peng-Jen Chen and Naji El Hachem and Brian Ellis and Gabriel Mejia Gonzalez and Justin Haaheim and Prangthip Hansanti and Russ Howes and Bernie Huang and Min-Jae Hwang and Hirofumi Inaguma and Somya Jain and Elahe Kalbassi and Amanda Kallet and Ilia Kulikov and Janice Lam and Daniel Li and Xutai Ma and Ruslan Mavlyutov and Benjamin Peloquin and Mohamed Ramadan and Abinesh Ramakrishnan and Anna Sun and Kevin Tran and Tuan Tran and Igor Tufanov and Vish Vogeti and Carleigh Wood and Yilin Yang and Bokai Yu and Pierre Andrews and Can Balioglu and Marta R. Costa-jussà and Onur Celebi and Maha Elbayad and Cynthia Gao and Francisco Guzmán and Justine Kao and Ann Lee and Alexandre Mourachko and Juan Pino and Sravya Popuri and Christophe Ropers and Safiyyah Saleem and Holger Schwenk and Paden Tomasello and Changhan Wang and Jeff Wang and Skyler Wang},
      year={2023},
      eprint={2308.11596},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{poria2019emotion,
      title={Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances}, 
      author={Soujanya Poria and Navonil Majumder and Rada Mihalcea and Eduard Hovy},
      year={2019},
      eprint={1905.02947},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{feng-etal-2022-language,
    title = "Language-agnostic {BERT} Sentence Embedding",
    author = "Feng, Fangxiaoyu  and
      Yang, Yinfei  and
      Cer, Daniel  and
      Arivazhagan, Naveen  and
      Wang, Wei",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.62",
    doi = "10.18653/v1/2022.acl-long.62",
    pages = "878--891",
    abstract = "While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM), dual encoder translation ranking, and additive margin softmax. We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80{\%}. Composing the best of these methods produces a model that achieves 83.7{\%} bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5{\%} achieved by LASER, while still performing competitively on monolingual transfer learning benchmarks. Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at \url{https://tfhub.dev/google/LaBSE}.",
}

@misc{lei2023instructerc,
      title={InstructERC: Reforming Emotion Recognition in Conversation with a Retrieval Multi-task LLMs Framework}, 
      author={Shanglin Lei and Guanting Dong and Xiaoping Wang and Keheng Wang and Sirui Wang},
      year={2023},
      eprint={2309.11911},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Antony2021EmotionRM,
  title={Emotion Recognition-Based Mental Healthcare Chat-bots: A Survey},
  author={Col Cj Antony and B Pariyath and Siti Noorfatimah Safar and Azharuddin Sahil and Nair Ar},
  journal={SSRN Electronic Journal},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:234209801}
}

@inproceedings{wadhawan-aggarwal-2021-towards,
    title = "Towards Emotion Recognition in {H}indi-{E}nglish Code-Mixed Data: A Transformer Based Approach",
    author = "Wadhawan, Anshul  and
      Aggarwal, Akshita",
    editor = "De Clercq, Orphee  and
      Balahur, Alexandra  and
      Sedoc, Joao  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Buechel, Sven  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.wassa-1.21",
    pages = "195--202",
    abstract = "In the last few years, emotion detection in social-media text has become a popular problem due to its wide ranging application in better understanding the consumers, in psychology, in aiding human interaction with computers, designing smart systems etc. Because of the availability of huge amounts of data from social-media, which is regularly used for expressing sentiments and opinions, this problem has garnered great attention. In this paper, we present a Hinglish dataset labelled for emotion detection. We highlight a deep learning based approach for detecting emotions using bilingual word embeddings derived from FastText and Word2Vec approaches in Hindi-English code mixed tweets. We experiment with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention), along with transformers like BERT, RoBERTa, and ALBERT. The transformer based BERT model outperforms all current state-of-the-art models giving the best performance with an accuracy of 71.43{\%}.",
}

@inproceedings{tu-etal-2023-empirical,
    title = "An Empirical Study on Multiple Knowledge from {C}hat{GPT} for Emotion Recognition in Conversations",
    author = "Tu, Geng  and
      Liang, Bin  and
      Qin, Bing  and
      Wong, Kam-Fai  and
      Xu, Ruifeng",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.813",
    doi = "10.18653/v1/2023.findings-emnlp.813",
    pages = "12160--12173",
    abstract = "Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large Language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge.",
}

@ARTICLE {masac,
author = {M. Bedi and S. Kumar and M. Akhtar and T. Chakraborty},
journal = {IEEE Transactions on Affective Computing},
title = {Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations},
year = {2023},
volume = {14},
number = {02},
issn = {1949-3045},
pages = {1363-1375},
abstract = {Sarcasm detection and humor classification are inherently subtle problems, primarily due to their dependence on the contextual and non-verbal information. Furthermore, existing studies in these two topics are usually constrained in non-English languages such as Hindi, due to the unavailability of qualitative annotated datasets. In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset, MaSaC,1 for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we propose MSH-COMICS,2 a novel attention-rich neural architecture for the utterance classification. We learn efficient utterance representation utilizing a hierarchical attention mechanism that attends to a small portion of the input sentence at a time. Further, we incorporate dialog-level contextual attention mechanism to leverage the dialog history for the multi-modal classification. We perform extensive experiments for both the tasks by varying multi-modal inputs and various submodules of MSH-COMICS. We also conduct comparative analysis against existing approaches. We observe that MSH-COMICS attains superior performance over the existing models by $&gt;$&gt;1 F1-score point for the sarcasm detection and 10 F1-score points in humor classification. We diagnose our model and perform thorough analysis of the results to understand the superiority and pitfalls.},
keywords = {task analysis;visualization;semantics;context modeling;acoustics;switches;planning},
doi = {10.1109/TAFFC.2021.3083522},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {apr}
}

@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} }
