{"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/weakly-supervised-formula-learner-for-solving","title":"Weakly Supervised Formula Learner for Solving Mathematical Problems","arxiv_id":null,"date":"2022-10-01","proceeding":"COLING 2022 10","authors":["Yuxuan Wu","Hideki Nakayama"],"abstract":"Mathematical reasoning task is a subset of the natural language question answering task. Existing work suggested solving this task with a two-phase approach, where the model first predicts formulas from questions and then calculates answers from such formulas. This approach achieved desirable performance in existing work. However, its reliance on annotated formulas as intermediate labels throughout its training limited its application. In this work, we put forward the idea to enable models to learn optimal formulas autonomously. We proposed Weakly Supervised Formula Learner, a learning framework that drives the formula exploration with weak supervision from the final answers to mathematical problems. Our experiments are conducted on two representative mathematical reasoning datasets MathQA and Math23K. On MathQA, our method outperformed baselines trained on complete yet imperfect formula annotations. On Math23K, our method outperformed other weakly supervised learning methods.","url_abs":"https://aclanthology.org/2022.coling-1.150","url_pdf":"https://aclanthology.org/2022.coling-1.150.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":"weakly-supervised-formula-learner-for-solving","repo_url":"https://github.com/evan-ak/wsfl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"mathematical-reasoning","task_name":"Mathematical Reasoning"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}