{"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/modeling-intra-relation-in-math-word-problems","title":"Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions","arxiv_id":null,"date":"2019-07-01","proceeding":"ACL 2019 7","authors":["Jierui Li","Lei Wang","Jipeng Zhang","Yan Wang","Bing Tian Dai","Dongxiang Zhang"],"abstract":"Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs{'} specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9{\\%} to 69.5{\\%} on Math23K with training-test split, from 65.8{\\%} to 66.9{\\%} on Math23K with 5-fold cross-validation and from 69.2{\\%} to 76.1{\\%} on MAWPS.","url_abs":"https://aclanthology.org/P19-1619","url_pdf":"https://aclanthology.org/P19-1619.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":"modeling-intra-relation-in-math-word-problems","repo_url":"https://github.com/lijierui/group-attention","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"math","task_name":"Math"},{"task_slug":"math-word-problem-solving","task_name":"Math Word Problem Solving"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/math-word-problem-solving-on-math23k","task":"Math Word Problem Solving","dataset":"Math23K","model":"GROUP-ATT","rank_in_archive_order":14,"of":19,"metrics":{"Accuracy (5-fold)":"66.9","Accuracy (training-test)":"69.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}