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        <title>Learning Fine-Grained Expressions to Solve Math Word Problems</title>
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        <namePart type="given">Danqing</namePart>
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        <namePart type="given">Shuming</namePart>
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    <name type="personal">
        <namePart type="given">Chin-Yew</namePart>
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    <name type="personal">
        <namePart type="given">Jian</namePart>
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    <abstract>This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset.</abstract>
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%0 Conference Proceedings
%T Learning Fine-Grained Expressions to Solve Math Word Problems
%A Huang, Danqing
%A Shi, Shuming
%A Lin, Chin-Yew
%A Yin, Jian
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F huang-etal-2017-learning
%X This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset.
%R 10.18653/v1/D17-1084
%U https://aclanthology.org/D17-1084
%U https://doi.org/10.18653/v1/D17-1084
%P 805-814

Markdown (Informal)

[Learning Fine-Grained Expressions to Solve Math Word Problems](https://aclanthology.org/D17-1084) (Huang et al., EMNLP 2017)

• Learning Fine-Grained Expressions to Solve Math Word Problems (Huang et al., EMNLP 2017)

ACL

• Danqing Huang, Shuming Shi, Chin-Yew Lin, and Jian Yin. 2017. Learning Fine-Grained Expressions to Solve Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 805–814, Copenhagen, Denmark. Association for Computational Linguistics.