{"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/dual-co-matching-network-for-multi-choice","title":"Dual Co-Matching Network for Multi-choice Reading Comprehension","arxiv_id":"1901.09381","date":"2019-01-27","proceeding":null,"authors":["Shuailiang Zhang","Hai Zhao","Yuwei Wu","Zhuosheng Zhang","Xi Zhou","Xiang Zhou"],"abstract":"Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \\textbf{D}ual \\textbf{C}o-\\textbf{M}atching \\textbf{N}etwork (\\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.","url_abs":"https://arxiv.org/abs/1901.09381v2","url_pdf":"https://arxiv.org/pdf/1901.09381v2.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":"multiple-choice","task_name":"Multiple-choice"},{"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-race","task":"Question Answering","dataset":"RACE","model":"DCMN_large","rank_in_archive_order":3,"of":7,"metrics":{"RACE":"69.7","RACE-h":"68.1","RACE-m":"73.4"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.09381","atlas_url":"https://app.syntology.ai/?focus=1901.09381","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}