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: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7322; https://ink.library.smu.edu.sg/context/sis_research/article/8325/viewcontent/MWPToolkit.pdf
• Institution: Singapore Management University

Duplicate record, see https://ink.library.smu.edu.sg/sis_research/7680/. Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked solely on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple their models into highly reusable modules. We also provide a hyper-parameter search function to boost the performance. In total, we implement and compare 17 MWP solvers on 4 widely-used single equation generation benchmarks and 2 multiple equations generation benchmarks. These features enable our MWPToolkit to be suitable for researchers to reproduce advanced baseline models and develop new MWP solvers quickly.