{"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/flowqa-grasping-flow-in-history-for","title":"FlowQA: Grasping Flow in History for Conversational Machine Comprehension","arxiv_id":"1810.06683","date":"2018-10-06","proceeding":"ICLR 2019 5","authors":["Hsin-Yuan Huang","Eunsol Choi","Wen-tau Yih"],"abstract":"Conversational machine comprehension requires the understanding of the\nconversation history, such as previous question/answer pairs, the document\ncontext, and the current question. To enable traditional, single-turn models to\nencode the history comprehensively, we introduce Flow, a mechanism that can\nincorporate intermediate representations generated during the process of\nanswering previous questions, through an alternating parallel processing\nstructure. Compared to approaches that concatenate previous questions/answers\nas input, Flow integrates the latent semantics of the conversation history more\ndeeply. Our model, FlowQA, shows superior performance on two recently proposed\nconversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The\neffectiveness of Flow also shows in other tasks. By reducing sequential\ninstruction understanding to conversational machine comprehension, FlowQA\noutperforms the best models on all three domains in SCONE, with +1.8% to +4.4%\nimprovement in accuracy.","url_abs":"http://arxiv.org/abs/1810.06683v3","url_pdf":"http://arxiv.org/pdf/1810.06683v3.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":"flowqa-grasping-flow-in-history-for","repo_url":"https://github.com/momohuang/FlowQA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-coqa","task":"Question Answering","dataset":"CoQA","model":"FlowQA (single model)","rank_in_archive_order":6,"of":9,"metrics":{"Out-of-domain":"71.8","Overall":"75.0"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-quac","task":"Question Answering","dataset":"QuAC","model":"FlowQA (single model)","rank_in_archive_order":1,"of":2,"metrics":{"F1":"64.1","HEQD":"5.8","HEQQ":"59.6"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.06683","atlas_url":"https://app.syntology.ai/?focus=1810.06683","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}