{"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/multi-passage-machine-reading-comprehension","title":"Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification","arxiv_id":"1805.02220","date":"2018-05-06","proceeding":"ACL 2018 7","authors":["Yizhong Wang","Kai Liu","Jing Liu","wei he","Yajuan Lyu","Hua Wu","Sujian Li","Haifeng Wang"],"abstract":"Machine reading comprehension (MRC) on real web data usually requires the\nmachine to answer a question by analyzing multiple passages retrieved by search\nengine. Compared with MRC on a single passage, multi-passage MRC is more\nchallenging, since we are likely to get multiple confusing answer candidates\nfrom different passages. To address this problem, we propose an end-to-end\nneural model that enables those answer candidates from different passages to\nverify each other based on their content representations. Specifically, we\njointly train three modules that can predict the final answer based on three\nfactors: the answer boundary, the answer content and the cross-passage answer\nverification. The experimental results show that our method outperforms the\nbaseline by a large margin and achieves the state-of-the-art performance on the\nEnglish MS-MARCO dataset and the Chinese DuReader dataset, both of which are\ndesigned for MRC in real-world settings.","url_abs":"http://arxiv.org/abs/1805.02220v2","url_pdf":"http://arxiv.org/pdf/1805.02220v2.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":[],"tasks":[{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-ms-marco","task":"Question Answering","dataset":"MS MARCO","model":"VNET","rank_in_archive_order":3,"of":4,"metrics":{"BLEU-1":"54.37","Rouge-L":"51.63"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.02220","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}