{"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/directed-acyclic-graph-network-for","title":"Directed Acyclic Graph Network for Conversational Emotion Recognition","arxiv_id":"2105.12907","date":"2021-05-27","proceeding":"ACL 2021 5","authors":["Weizhou Shen","Siyue Wu","Yunyi Yang","Xiaojun Quan"],"abstract":"The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.","url_abs":"https://arxiv.org/abs/2105.12907v2","url_pdf":"https://arxiv.org/pdf/2105.12907v2.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":"directed-acyclic-graph-network-for","repo_url":"https://github.com/shenwzh3/DAG-ERC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-3","task":"Emotion Recognition in Conversation","dataset":"DailyDialog","model":"DAG-ERC","rank_in_archive_order":10,"of":22,"metrics":{"Micro-F1":"59.33"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-4","task":"Emotion Recognition in Conversation","dataset":"EmoryNLP","model":"DAG-ERC","rank_in_archive_order":14,"of":28,"metrics":{"Weighted-F1":"39.02"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on","task":"Emotion Recognition in Conversation","dataset":"IEMOCAP","model":"DAG-ERC","rank_in_archive_order":29,"of":59,"metrics":{"Weighted-F1":"68.03"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-meld","task":"Emotion Recognition in Conversation","dataset":"MELD","model":"DAG-ERC","rank_in_archive_order":42,"of":68,"metrics":{"Weighted-F1":"63.65"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.12907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12907"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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