{"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/emotionic-emotional-inertia-and-contagion","title":"EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation","arxiv_id":"2303.11117","date":"2023-03-20","proceeding":null,"authors":["Yingjian Liu","Jiang Li","XiaoPing Wang","Zhigang Zeng"],"abstract":"Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU), and Skip-chain Conditional Random Field (SkipCRF). Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention- and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while DiaGRU is utilized to extract speaker- and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets. The ablation studies confirm that our modules can effectively model emotional inertia and contagion.","url_abs":"https://arxiv.org/abs/2303.11117v5","url_pdf":"https://arxiv.org/pdf/2303.11117v5.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":"emotionic-emotional-inertia-and-contagion","repo_url":"https://github.com/lijfrank-open/EmotionIC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"emotion-recognition-in-conversation","task_name":"Emotion Recognition in Conversation"}],"methods":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-3","task":"Emotion Recognition in Conversation","dataset":"DailyDialog","model":"EmotionIC","rank_in_archive_order":7,"of":22,"metrics":{"Macro F1":"54.19","Micro-F1":"60.13"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-4","task":"Emotion Recognition in Conversation","dataset":"EmoryNLP","model":"EmotionIC","rank_in_archive_order":6,"of":28,"metrics":{"Micro-F1":"44.31","Weighted-F1":"40.25"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on","task":"Emotion Recognition in Conversation","dataset":"IEMOCAP","model":"EmotionIC","rank_in_archive_order":21,"of":59,"metrics":{"Accuracy":"69.44","Weighted-F1":"69.61"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-meld","task":"Emotion Recognition in Conversation","dataset":"MELD","model":"EmotionIC","rank_in_archive_order":23,"of":68,"metrics":{"Micro-F1":"67.59","Weighted-F1":"66.32"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.11117","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}