@inproceedings{chiang-chen-2019-semantically,
title = "Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems",
author = "Chiang, Ting-Rui and
Chen, Yun-Nung",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1272",
doi = "10.18653/v1/N19-1272",
pages = "2656--2668",
abstract = "Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10{\%} accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.",
}
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%0 Conference Proceedings
%T Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems
%A Chiang, Ting-Rui
%A Chen, Yun-Nung
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F chiang-chen-2019-semantically
%X Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10% accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.
%R 10.18653/v1/N19-1272
%U https://aclanthology.org/N19-1272
%U https://doi.org/10.18653/v1/N19-1272
%P 2656-2668
Markdown (Informal)
[Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems](https://aclanthology.org/N19-1272) (Chiang & Chen, NAACL 2019)
ACL