Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2020

Abstract

Math word problem (MWP) is challenging due to the limitation in training data where only one “standard” solution is available. MWP models often simply fit this solution rather than truly understand or solve the problem. The generalization of models (to diverse word scenarios) is thus limited. To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network. In addition, we introduce the multiple-decoder student network to generate multiple candidate solution expressions by which the final answer is voted. In experiments, we conduct extensive comparisons and ablative studies on two large-scale MWP benchmarks, and show that using TSN-MD can surpass the state-of-the-art works by a large margin. More intriguingly, the visualization results demonstrate that TSN-MD not only produces correct final answers but also generates diverse equivalent expressions of the solution.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Mathematics | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence 2020: Yokohama

First Page

4011

Last Page

4017

Identifier

10.24963/ijcai.2020/555

Publisher

IJCAI

City or Country

Menlo Park, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2020/555

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