@inproceedings{wang-etal-2022-structure,
title = "Structure-Unified {M}-Tree Coding Solver for Math Word Problem",
author = "Wang, Bin and
Ju, Jiangzhou and
Fan, Yang and
Dai, Xinyu and
Huang, Shujian and
Chen, Jiajun",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.556",
doi = "10.18653/v1/2022.emnlp-main.556",
pages = "8122--8132",
abstract = "As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years. In previous work, models designed by taking into account the properties of the binary tree structure of mathematical expressions at the output side have achieved better performance. However, the expressions corresponding to a MWP are often diverse (e.g., $n_1+n_2 \times n_3-n_4$, $n_3\times n_2-n_4+n_1$, etc.), and so are the corresponding binary trees, which creates difficulties in model learning due to the non-deterministic output space. In this paper, we propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies a tree with any M branches (M-tree) to unify the output structures. To learn the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where codes store the information of the paths from tree root to leaf nodes and the information of leaf nodes themselves, and then devise a Sequence-to-Code (seq2code) model to generate the codes. Experimental results on the widely used MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only outperforms several state-of-the-art models under similar experimental settings but also performs much better under low-resource conditions.",
}
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<abstract>As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years. In previous work, models designed by taking into account the properties of the binary tree structure of mathematical expressions at the output side have achieved better performance. However, the expressions corresponding to a MWP are often diverse (e.g., n₁+n₂ \times n₃-n₄, n₃\times n₂-n₄+n₁, etc.), and so are the corresponding binary trees, which creates difficulties in model learning due to the non-deterministic output space. In this paper, we propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies a tree with any M branches (M-tree) to unify the output structures. To learn the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where codes store the information of the paths from tree root to leaf nodes and the information of leaf nodes themselves, and then devise a Sequence-to-Code (seq2code) model to generate the codes. Experimental results on the widely used MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only outperforms several state-of-the-art models under similar experimental settings but also performs much better under low-resource conditions.</abstract>
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%0 Conference Proceedings
%T Structure-Unified M-Tree Coding Solver for Math Word Problem
%A Wang, Bin
%A Ju, Jiangzhou
%A Fan, Yang
%A Dai, Xinyu
%A Huang, Shujian
%A Chen, Jiajun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-structure
%X As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years. In previous work, models designed by taking into account the properties of the binary tree structure of mathematical expressions at the output side have achieved better performance. However, the expressions corresponding to a MWP are often diverse (e.g., n₁+n₂ \times n₃-n₄, n₃\times n₂-n₄+n₁, etc.), and so are the corresponding binary trees, which creates difficulties in model learning due to the non-deterministic output space. In this paper, we propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies a tree with any M branches (M-tree) to unify the output structures. To learn the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where codes store the information of the paths from tree root to leaf nodes and the information of leaf nodes themselves, and then devise a Sequence-to-Code (seq2code) model to generate the codes. Experimental results on the widely used MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only outperforms several state-of-the-art models under similar experimental settings but also performs much better under low-resource conditions.
%R 10.18653/v1/2022.emnlp-main.556
%U https://aclanthology.org/2022.emnlp-main.556
%U https://doi.org/10.18653/v1/2022.emnlp-main.556
%P 8122-8132
Markdown (Informal)
[Structure-Unified M-Tree Coding Solver for Math Word Problem](https://aclanthology.org/2022.emnlp-main.556) (Wang et al., EMNLP 2022)
ACL
- Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2022. Structure-Unified M-Tree Coding Solver for Math Word Problem. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8122–8132, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.