{"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/transition-based-directed-graph-construction","title":"Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction","arxiv_id":null,"date":"2020-07-01","proceeding":"ACL 2020 6","authors":["Chuang Fan","Chaofa Yuan","Jiachen Du","Lin Gui","Min Yang","Ruifeng Xu"],"abstract":"Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71{\\%} (p{\\textless}0.01) in F1 measure.","url_abs":"https://aclanthology.org/2020.acl-main.342","url_pdf":"https://aclanthology.org/2020.acl-main.342.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":"transition-based-directed-graph-construction","repo_url":"https://github.com/HLT-HITSZ/TransECPE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"emotion-cause-pair-extraction","task_name":"Emotion-Cause Pair Extraction"},{"task_slug":"graph-construction","task_name":"graph construction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-cause-pair-extraction-on-ecpe-1","task":"Emotion-Cause Pair Extraction","dataset":"ECPE-FanSplit","model":"Transition-based Directed Graph","rank_in_archive_order":2,"of":2,"metrics":{"F1":"67.99"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}