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Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly performing relational reasoning between quantities in the given context. While empirically effective, such approaches typically do not provide explanations for the generated expressions. In this work, we view the task as a complex relation extraction problem, proposing a novel approach that presents explainable deductive reasoning steps to iteratively construct target expressions, where each step involves a primitive operation over two quantities defining their relation. Through extensive experiments on four benchmark datasets, we show that the proposed model significantly outperforms existing strong baselines. We further demonstrate that the deductive procedure not only presents more explainable steps but also enables us to make more accurate predictions on questions that require more complex reasoning.","url_abs":"https://arxiv.org/abs/2203.10316v4","url_pdf":"https://arxiv.org/pdf/2203.10316v4.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":"learning-to-reason-deductively-math-word","repo_url":"https://github.com/allanj/deductive-mwp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"math","task_name":"Math"},{"task_slug":"math-word-problem-solving","task_name":"Math Word Problem Solving"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/math-word-problem-solving-on-mawps","task":"Math Word Problem Solving","dataset":"MAWPS","model":"Roberta-DeductReasoner","rank_in_archive_order":7,"of":25,"metrics":{"Accuracy (%)":"92"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-math23k","task":"Math Word Problem Solving","dataset":"Math23K","model":"Roberta-DeductReasoner","rank_in_archive_order":6,"of":19,"metrics":{"Accuracy (5-fold)":"83"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-mathqa","task":"Math Word Problem Solving","dataset":"MathQA","model":"Roberta-DeductReasoner","rank_in_archive_order":4,"of":5,"metrics":{"Answer Accuracy":"78.6"},"uses_additional_data":false},{"leaderboard":"/sota/math-word-problem-solving-on-svamp","task":"Math Word Problem Solving","dataset":"SVAMP","model":"Roberta-DeductReasoner","rank_in_archive_order":19,"of":26,"metrics":{"Execution Accuracy":"47.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.10316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10316"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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