{"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/generating-equation-by-utilizing-operators","title":"Generating Equation by Utilizing Operators : GEO model","arxiv_id":null,"date":"2020-12-01","proceeding":"COLING 2020 8","authors":["Kyung Seo Ki","Donggeon Lee","Bugeun Kim","Gahgene Gweon"],"abstract":"Math word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly used in machine translation tasks. The state-of-the-art neural models use hand-crafted features and are based on generation methods. In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge. To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction. To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer. Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1{\\%} in MAWPS, and 62.5{\\%} in DRAW-1K, and reached comparable performance of 82.1{\\%} in ALG514 dataset.","url_abs":"https://aclanthology.org/2020.coling-main.38","url_pdf":"https://aclanthology.org/2020.coling-main.38.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":"decoder","task_name":"Decoder"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"math","task_name":"Math"},{"task_slug":"math-word-problem-solving","task_name":"Math Word Problem Solving"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/math-word-problem-solving-on-alg514","task":"Math Word Problem Solving","dataset":"ALG514","model":"GEO","rank_in_archive_order":2,"of":10,"metrics":{"Accuracy (%)":"82.1"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-draw-1k","task":"Math Word Problem Solving","dataset":"DRAW-1K","model":"GEO","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy (%)":"62.5"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-mawps","task":"Math Word Problem Solving","dataset":"MAWPS","model":"GEO","rank_in_archive_order":12,"of":25,"metrics":{"Accuracy (%)":"85.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}