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        <title>Solving Math Word Problems with Multi-Encoders and Multi-Decoders</title>
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    <abstract>Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder. Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions. To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder. Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.262</identifier>
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%0 Conference Proceedings
%T Solving Math Word Problems with Multi-Encoders and Multi-Decoders
%A Shen, Yibin
%A Jin, Cheqing
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F shen-jin-2020-solving
%X Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder. Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions. To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder. Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.
%R 10.18653/v1/2020.coling-main.262
%U https://aclanthology.org/2020.coling-main.262
%U https://doi.org/10.18653/v1/2020.coling-main.262
%P 2924-2934

Markdown (Informal)

[Solving Math Word Problems with Multi-Encoders and Multi-Decoders](https://aclanthology.org/2020.coling-main.262) (Shen & Jin, COLING 2020)

• Solving Math Word Problems with Multi-Encoders and Multi-Decoders (Shen & Jin, COLING 2020)

ACL

• Yibin Shen and Cheqing Jin. 2020. Solving Math Word Problems with Multi-Encoders and Multi-Decoders. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2924–2934, Barcelona, Spain (Online). International Committee on Computational Linguistics.