{"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/an-end-to-end-network-for-emotion-cause-pair","title":"An End-to-End Network for Emotion-Cause Pair Extraction","arxiv_id":"2103.01544","date":"2021-03-02","proceeding":null,"authors":["Aaditya Singh","Shreeshail Hingane","Saim Wani","Ashutosh Modi"],"abstract":"The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (~6.5 increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2103.01544v2","url_pdf":"https://arxiv.org/pdf/2103.01544v2.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":"an-end-to-end-network-for-emotion-cause-pair","repo_url":"https://github.com/Aaditya-Singh/E2E-ECPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"emotion-cause-extraction","task_name":"Emotion Cause Extraction"},{"task_slug":"emotion-cause-pair-extraction","task_name":"Emotion-Cause Pair Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.01544","atlas_url":"https://app.syntology.ai/?focus=2103.01544","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}