{"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/question-directed-graph-attention-network-for","title":"Question Directed Graph Attention Network for Numerical Reasoning over Text","arxiv_id":"2009.07448","date":"2020-09-16","proceeding":"EMNLP 2020 11","authors":["Kunlong Chen","Weidi Xu","Xingyi Cheng","Zou Xiaochuan","Yuyu Zhang","Le Song","Taifeng Wang","Yuan Qi","Wei Chu"],"abstract":"Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. The code link is at: https://github.com/emnlp2020qdgat/QDGAT","url_abs":"https://arxiv.org/abs/2009.07448v2","url_pdf":"https://arxiv.org/pdf/2009.07448v2.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":"graph-attention","task_name":"Graph Attention"},{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"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-drop-test","task":"Question Answering","dataset":"DROP Test","model":"QDGAT (ensemble)","rank_in_archive_order":1,"of":16,"metrics":{"F1":"88.38"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2009.07448","atlas_url":"https://app.syntology.ai/?focus=2009.07448","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}