Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2022

Abstract

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learning-based MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https://github.com/LYH-YF/MWPToolkit.

Keywords

Math Word Problem Solving, Deep Learning, Toolkit

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 36th AAAI Conference on Artificial Intelligence was held virtually, 2022 February 22-March 1

Volume

36

First Page

13188

Last Page

13190

Identifier

10.1609/aaai.v36i11.21723

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

https://doi.org/10.1609/aaai.v36i11.21723

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