{"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/effective-inter-clause-modeling-for-end-to","title":"Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction","arxiv_id":null,"date":"2020-07-01","proceeding":"ACL 2020 6","authors":["Penghui Wei","Jiahao Zhao","Wenji Mao"],"abstract":"Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.","url_abs":"https://aclanthology.org/2020.acl-main.289","url_pdf":"https://aclanthology.org/2020.acl-main.289.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":"effective-inter-clause-modeling-for-end-to","repo_url":"https://github.com/Determined22/Rank-Emotion-Cause","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-cause-pair-extraction","task_name":"Emotion-Cause Pair Extraction"},{"task_slug":"graph-attention","task_name":"Graph Attention"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-cause-pair-extraction-on-ecpe-1","task":"Emotion-Cause Pair Extraction","dataset":"ECPE-FanSplit","model":"RANKCP","rank_in_archive_order":1,"of":2,"metrics":{"F1":"69.15"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}