{"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/dialoguernn-an-attentive-rnn-for-emotion","title":"DialogueRNN: An Attentive RNN for Emotion Detection in Conversations","arxiv_id":"1811.00405","date":"2018-11-01","proceeding":null,"authors":["Navonil Majumder","Soujanya Poria","Devamanyu Hazarika","Rada Mihalcea","Alexander Gelbukh","Erik Cambria"],"abstract":"Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently, systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state of the art by a significant margin on two different datasets.","url_abs":"https://arxiv.org/abs/1811.00405v4","url_pdf":"https://arxiv.org/pdf/1811.00405v4.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":"dialoguernn-an-attentive-rnn-for-emotion","repo_url":"https://github.com/SenticNet/conv-emotion","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dialoguernn-an-attentive-rnn-for-emotion","repo_url":"https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition-in-conversation","task_name":"Emotion Recognition in Conversation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multimodal-emotion-recognition","task_name":"Multimodal Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-cped","task":"Emotion Recognition in Conversation","dataset":"CPED","model":"DialogueRNN","rank_in_archive_order":8,"of":11,"metrics":{"Accuracy of Sentiment":"48.57","Macro-F1 of Sentiment":"44.11"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on","task":"Emotion Recognition in Conversation","dataset":"IEMOCAP","model":"DialogueRNN","rank_in_archive_order":51,"of":59,"metrics":{"Accuracy":"63.5","Weighted-F1":"63.5"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-meld","task":"Emotion Recognition in Conversation","dataset":"MELD","model":"DialogueRNN","rank_in_archive_order":65,"of":68,"metrics":{"Accuracy":"59.54","Weighted-F1":"57.03"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-2","task":"Emotion Recognition in Conversation","dataset":"SEMAINE","model":"DialogueRNN","rank_in_archive_order":3,"of":3,"metrics":{"MAE (Arousal)":"0.165","MAE (Expectancy)":"0.175","MAE (Power)":"7.9","MAE (Valence)":"0.168"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00405","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}