Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning
Weijiang Yu, Yingpeng Wen, Fudan Zheng, Nong Xiao · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 pretrained 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 wordlevel 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.