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    <abstract>Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual variance by textual reordering or problem re-construction. We then retrieve the hardest to differentiate samples from both equation and textual perspectives and guide the model to learn their representations. Experimental results show that our method achieves state-of-the-art on both widely used benchmark datasets and also exquisitely designed challenge datasets in English and Chinese.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.316</identifier>
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%0 Conference Proceedings
%T Textual Enhanced Contrastive Learning for Solving Math Word Problems
%A Shen, Yibin
%A Liu, Qianying
%A Mao, Zhuoyuan
%A Cheng, Fei
%A Kurohashi, Sadao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F shen-etal-2022-textual
%X Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual variance by textual reordering or problem re-construction. We then retrieve the hardest to differentiate samples from both equation and textual perspectives and guide the model to learn their representations. Experimental results show that our method achieves state-of-the-art on both widely used benchmark datasets and also exquisitely designed challenge datasets in English and Chinese.
%R 10.18653/v1/2022.findings-emnlp.316
%U https://aclanthology.org/2022.findings-emnlp.316
%U https://doi.org/10.18653/v1/2022.findings-emnlp.316
%P 4297-4307

Markdown (Informal)

[Textual Enhanced Contrastive Learning for Solving Math Word Problems](https://aclanthology.org/2022.findings-emnlp.316) (Shen et al., Findings 2022)

• Textual Enhanced Contrastive Learning for Solving Math Word Problems (Shen et al., Findings 2022)

ACL

• Yibin Shen, Qianying Liu, Zhuoyuan Mao, Fei Cheng, and Sadao Kurohashi. 2022. Textual Enhanced Contrastive Learning for Solving Math Word Problems. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4297–4307, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.