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Translating a Math Word Problem to an Expression Tree

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arxiv 1811.05632 v2 pith:QDNVBRYS submitted 2018-11-14 cs.CL

Lei Wang , Yan Wang , Deng Cai , Dongxiang Zhang , Xiaojiang Liu This is my paper
classification cs.CL
keywords mathproblemwordmodelsolvingensembleequationequations
verification ladder T0 review T1 audit T2 compute T3 formal
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Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery

    cs.AI 2026-06 unverdicted novelty 5.0 of 10

    An integrated survey organizing AI mathematical reasoning into informal, formal, discovery, and technique axes while cataloging benchmarks and assessing failure modes.

  2. We Need Knowledge Distillation for Solving Math Word Problems

    cs.CL 2025-07 reject novelty 4.0 of 10

    A 3-layer student transformer distilled from compressed BERT vectors retains roughly 90 percent of teacher performance on Math23K, according to the authors, though the teacher baseline is not shown.

  3. Leveraging Large Language Models for Bengali Math Word Problem Solving with Chain of Thought Reasoning

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A new Bengali math word problem dataset translated from GSM8K is benchmarked with chain-of-thought prompting, yielding 88% accuracy with LLaMA-3.3 70B on a 1,000-sample test subset.

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