{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recognizing-emotion-cause-in-conversations-1","title":"Recognizing Emotion Cause in Conversations","arxiv_id":"2012.11820","date":"2020-12-22","proceeding":null,"authors":["Soujanya Poria","Navonil Majumder","Devamanyu Hazarika","Deepanway Ghosal","Rishabh Bhardwaj","Samson Yu Bai Jian","Pengfei Hong","Romila Ghosh","Abhinaba Roy","Niyati Chhaya","Alexander Gelbukh","Rada Mihalcea"],"abstract":"We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Introduction: Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. Method: We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause-effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset: causal span extraction and causal emotion entailment. Result: Our Transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches Conclusion: We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.","url_abs":"https://arxiv.org/abs/2012.11820v4","url_pdf":"https://arxiv.org/pdf/2012.11820v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recognizing-emotion-cause-in-conversations-1","repo_url":"https://github.com/declare-lab/RECCON","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"causal-emotion-entailment","task_name":"Causal Emotion Entailment"},{"task_slug":"emotion-cause-extraction","task_name":"Emotion Cause Extraction"},{"task_slug":"recognizing-emotion-cause-in-conversations","task_name":"Recognizing Emotion Cause in Conversations"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"causal-inference","method_name":"Causal inference"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"interpretability","method_name":"Interpretability"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roberta","method_name":"RoBERTa"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"reccon","name":"RECCON","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/causal-emotion-entailment-on-reccon","task":"Causal Emotion Entailment","dataset":"RECCON","model":"RoBERTa Large","rank_in_archive_order":8,"of":9,"metrics":{"Macro F1":"77.06","Neg. F1":"87.89","Pos. F1":"66.23"},"uses_additional_data":false},{"leaderboard":"/sota/causal-emotion-entailment-on-reccon","task":"Causal Emotion Entailment","dataset":"RECCON","model":"RoBERTa Base","rank_in_archive_order":9,"of":9,"metrics":{"Macro F1":"76.51","Neg. F1":"88.74","Pos. F1":"64.28"},"uses_additional_data":false},{"leaderboard":"/sota/recognizing-emotion-cause-in-conversations-on","task":"Recognizing Emotion Cause in Conversations","dataset":"RECCON","model":"SpanBERT","rank_in_archive_order":1,"of":2,"metrics":{"Exact Span F1":"34.64","F1":"75.71","F1(Neg)":"86.02","F1(Pos)":"60.00"},"uses_additional_data":false},{"leaderboard":"/sota/recognizing-emotion-cause-in-conversations-on","task":"Recognizing Emotion Cause in Conversations","dataset":"RECCON","model":"RoBERTa Base","rank_in_archive_order":2,"of":2,"metrics":{"Exact Span F1":"32.63","F1":"75.45","F1(Neg)":"85.85","F1(Pos)":"58.17"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2012.11820","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}