MWPToolkit: An open-source framework for deep learning-based math word problem solvers

• Main Authors: LAN, Yihuai, WANG, Lei, ZHANG, Qiyuan, LAN, Yunshi, DAI, Bing Tian, WANG, Yan, ZHANG, Dongxiang, Ee-peng LIM
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Math Word Problem Solving; Deep Learning; Toolkit; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7680; https://ink.library.smu.edu.sg/context/sis_research/article/8683/viewcontent/MWPToolkit.pdf
• Institution: Singapore Management University

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.