{"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/asking-effective-and-diverse-questions-a","title":"Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction","arxiv_id":null,"date":"2020-07-01","proceeding":"IJCAI 2020 7","authors":["Tianyang Zhao","Zhao Yan","Yunbo Cao","Zhoujun Li"],"abstract":"Recent advances cast the entity-relation extraction\r\nto a multi-turn question answering (QA) task and\r\nprovide an effective solution based on the machine\r\nreading comprehension (MRC) models. However,\r\nthey use a single question to characterize the meaning of entities and relations, which is intuitively\r\nnot enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which\r\nis inefficient and easily leads to confusing questions. In this paper, we improve the existing MRCbased entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect\r\nentity spans and two answering selection strategies\r\nare designed to integrate different answers. Then,\r\nwe propose to predict a subset of potential relations\r\nand filter out irrelevant ones to generate questions\r\neffectively. Finally, entity and relation extractions\r\nare integrated in an end-to-end way and optimized\r\nthrough joint learning. Experiment results show\r\nthat the proposed method significantly outperforms\r\nbaseline models, which improves the relation F1\r\nto 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%)\r\non CoNLL04. Our implementation is available at\r\nhttps://github.com/TanyaZhao/MRC4ERE.","url_abs":"https://www.ijcai.org/Proceedings/2020/0546.pdf","url_pdf":"https://www.ijcai.org/Proceedings/2020/0546.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":"asking-effective-and-diverse-questions-a","repo_url":"https://github.com/TanyaZhao/MRC4ERE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-ace-2005","task":"Relation Extraction","dataset":"ACE 2005","model":"MRC4ERE++","rank_in_archive_order":16,"of":30,"metrics":{"Cross Sentence":"No","NER Micro F1":"85.5","RE+ Micro F1":"62.1","Sentence Encoder":"BERT base"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}