Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

Abstract

While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree’s effectiveness in translating the MWP text into solution expressions.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

First Page

3928

Last Page

3937

Identifier

10.18653/v1/2020.acl-main.362

Publisher

Association for Computational Linguistics

City or Country

Online

Additional URL

https://doi.org/10.18653/v1/2020.acl-main.362

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