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    <name type="personal">
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    <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</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.108</identifier>
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%0 Conference Proceedings
%T A Generation-based Deductive Method for Math Word Problems
%A Hu, Yuxuan
%A Zhang, Jing
%A Li, Haoyang
%A Li, Cuiping
%A Chen, Hong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hu-etal-2023-generation
%X 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
%R 10.18653/v1/2023.emnlp-main.108
%U https://aclanthology.org/2023.emnlp-main.108
%U https://doi.org/10.18653/v1/2023.emnlp-main.108
%P 1737-1750

Markdown (Informal)

[A Generation-based Deductive Method for Math Word Problems](https://aclanthology.org/2023.emnlp-main.108) (Hu et al., EMNLP 2023)

• A Generation-based Deductive Method for Math Word Problems (Hu et al., EMNLP 2023)

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.