@inproceedings{yu-etal-2021-improving,
title = "Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning",
author = "Yu, Weijiang and
Wen, Yingpeng and
Zheng, Fudan and
Xiao, Nong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.272",
doi = "10.18653/v1/2021.emnlp-main.272",
pages = "3384--3394",
abstract = "The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose a \textbf{R}easoning with \textbf{P}re-trained \textbf{K}nowledge and \textbf{H}ierarchical \textbf{S}tructure (\textbf{RPKHS}) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pre-trained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the word-level and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4{\%} to 83.9{\%} on Math23K, from 75.5 to 82.2{\%} on Math23K with 5-fold cross-validation and from 83.7{\%} to 89.8{\%} on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.",
}
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<abstract>The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pre-trained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the word-level and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.</abstract>
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%0 Conference Proceedings
%T Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning
%A Yu, Weijiang
%A Wen, Yingpeng
%A Zheng, Fudan
%A Xiao, Nong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F yu-etal-2021-improving
%X The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pre-trained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the word-level and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.
%R 10.18653/v1/2021.emnlp-main.272
%U https://aclanthology.org/2021.emnlp-main.272
%U https://doi.org/10.18653/v1/2021.emnlp-main.272
%P 3384-3394
Markdown (Informal)
[Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning](https://aclanthology.org/2021.emnlp-main.272) (Yu et al., EMNLP 2021)
ACL