{"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/point-to-the-expression-solving-algebraic","title":"Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model","arxiv_id":null,"date":"2020-11-01","proceeding":"EMNLP 2020 11","authors":["Bugeun Kim","Kyung Seo Ki","Donggeon Lee","Gahgene Gweon"],"abstract":"Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using {`}Op (operator/operand){'} tokens as a unit of input/output. However, such a neural model suffered two issues: expression fragmentation and operand-context separation. To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) {`}Expression{'} token and (2) operand-context pointers when generating solution equations. The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS. Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3{\\%} on ALG514, 59.5{\\%} on DRAW-1K, and 84.5{\\%} on MAWPS. The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operand-context separation. (2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40{\\%}.","url_abs":"https://aclanthology.org/2020.emnlp-main.308","url_pdf":"https://aclanthology.org/2020.emnlp-main.308.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":"point-to-the-expression-solving-algebraic","repo_url":"https://github.com/snucclab/EPT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"math-word-problem-solving","task_name":"Math Word Problem Solving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/math-word-problem-solving-on-alg514","task":"Math Word Problem Solving","dataset":"ALG514","model":"EPT","rank_in_archive_order":3,"of":10,"metrics":{"Accuracy (%)":"81.31"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-draw-1k","task":"Math Word Problem Solving","dataset":"DRAW-1K","model":"EPT","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy (%)":"59.5"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-mawps","task":"Math Word Problem Solving","dataset":"MAWPS","model":"EPT","rank_in_archive_order":14,"of":25,"metrics":{"Accuracy (%)":"84.51"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}