A Generation-based Deductive Method for Math Word Problems

Yuxuan Hu, Jing Zhang, Haoyang Li, Cuiping Li, Hong Chen


Abstract
Math word problems (MWP) involving advanced operators such as linear equation solver cannot be easily tackled by earlier MWP methods, because the existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations. This paper propose a new multivariate directed acyclic graph (mDAG) as an alternative to the generation methods’ binary expression tree or the deductive methods’ binary directed acyclic graph. Then to produce the topological ordering of mDAG, we propose a generation-based deductive (GeDe) model, which equips a generation model with a re-encoder to keep the deductive property but avoid the expensive enumeration of the deductive methods. GeDe performs well on math problems with many operators on the widely used benchmarks as well as solving multivariate operators on our own CMWPA benchmark. Our code is available at https://github.com/hyx1999/GeDe
Anthology ID:
2023.emnlp-main.108
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1737–1750
Language:
URL:
https://aclanthology.org/2023.emnlp-main.108
DOI:
10.18653/v1/2023.emnlp-main.108
Bibkey:
Cite (ACL):
Yuxuan Hu, Jing Zhang, Haoyang Li, Cuiping Li, and Hong Chen. 2023. A Generation-based Deductive Method for Math Word Problems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1737–1750, Singapore. Association for Computational Linguistics.
Cite (Informal):
A Generation-based Deductive Method for Math Word Problems (Hu et al., EMNLP 2023)
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PDF:
https://aclanthology.org/2023.emnlp-main.108.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.108.mp4