@inproceedings{shen-etal-2022-seeking,
title = "Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems",
author = "Shen, Yibin and
Liu, Qianying and
Mao, Zhuoyuan and
Wan, Zhen and
Cheng, Fei and
Kurohashi, Sadao",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.32",
pages = "254--260",
abstract = "To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions. However, the mainstream sequence-to-sequence approach of automatic solvers aims to decode a fixed solution equation supervised by human annotation. In this paper, we propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. The empirical results suggest that our method universally improves the performance on single-unknown (Math23K) and multiple-unknown (DRAW1K, HMWP) benchmarks, with substantial improvements up to 13.2{\%} accuracy on the challenging multiple-unknown datasets.",
}
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<abstract>To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions. However, the mainstream sequence-to-sequence approach of automatic solvers aims to decode a fixed solution equation supervised by human annotation. In this paper, we propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. The empirical results suggest that our method universally improves the performance on single-unknown (Math23K) and multiple-unknown (DRAW1K, HMWP) benchmarks, with substantial improvements up to 13.2% accuracy on the challenging multiple-unknown datasets.</abstract>
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%0 Conference Proceedings
%T Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
%A Shen, Yibin
%A Liu, Qianying
%A Mao, Zhuoyuan
%A Wan, Zhen
%A Cheng, Fei
%A Kurohashi, Sadao
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F shen-etal-2022-seeking
%X To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions. However, the mainstream sequence-to-sequence approach of automatic solvers aims to decode a fixed solution equation supervised by human annotation. In this paper, we propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. The empirical results suggest that our method universally improves the performance on single-unknown (Math23K) and multiple-unknown (DRAW1K, HMWP) benchmarks, with substantial improvements up to 13.2% accuracy on the challenging multiple-unknown datasets.
%U https://aclanthology.org/2022.aacl-short.32
%P 254-260
Markdown (Informal)
[Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems](https://aclanthology.org/2022.aacl-short.32) (Shen et al., AACL-IJCNLP 2022)
ACL