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TM-generation model: a template-based method for automatically solving mathematical word problems

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Abstract

In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word problem-solving task. In automatic mathematical word problem-solving task, a machine learning model should deduce an answer to a given problem by acquiring implied numeric information. To build a robust model that can sufficiently utilize numeric information to solve various mathematical word problems, such a model should address two challenges: (1) filling in missing world knowledge required to solve the given mathematical word problem, and (2) understanding the implied relationship between numbers and variables. To address these two challenges, we propose template-based multitask generation (TM-generation). To address challenge (1), we utilize the state-of-the-art language models called ELECTRA. To address challenge (2), we propose an operator identification layer that models the relationship between numbers and variables. Our experimental results show that using the MAWPS and Math23k datasets, state-of-the-art performance was achieved: 85.2% in MAWPS and 85.3% in Math23k.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1C1C1010162).

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  1. Seoul National University, Seoul, Republic of Korea

    Donggeon Lee, Kyung Seo Ki, Bugeun Kim & Gahgene Gweon

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  1. Donggeon Lee
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  2. Kyung Seo Ki
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  4. Gahgene Gweon
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Correspondence to Gahgene Gweon.

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Lee, D., Ki, K.S., Kim, B. et al. TM-generation model: a template-based method for automatically solving mathematical word problems. J Supercomput 77, 14583–14599 (2021). https://doi.org/10.1007/s11227-021-03855-9

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