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    <abstract>Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description. However, such structures fail to handle the variants that can be derived via mathematical manipulation, e.g., (a₁+a₂)*a₃ and a₁ * a₃+a₂ * a₃ can both be possible valid solutions for a same problem but formulated as different expression sequences or trees. The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating wrong when evaluating a valid expression variant. To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants. We propose a novel non-autoregressive solver, named MWP-NAS, to parse the problem and deduce the solution expression based on the unified tree. For evaluating the possible expression variants, we design a path-based metric to evaluate the partial accuracy of expressions of a unified tree. The results from extensive experiments conducted on Math23K and MAWPS demonstrate the effectiveness of our proposed MWP-NAS. The codes and checkpoints are available at: https://github.com/mengqunhan/MWP-NAS.</abstract>
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%0 Conference Proceedings
%T Non-Autoregressive Math Word Problem Solver with Unified Tree Structure
%A Bin, Yi
%A Han, Mengqun
%A Shi, Wenhao
%A Wang, Lei
%A Yang, Yang
%A Ng, See-Kiong
%A Shen, Heng
%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 bin-etal-2023-non
%X Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description. However, such structures fail to handle the variants that can be derived via mathematical manipulation, e.g., (a₁+a₂)*a₃ and a₁ * a₃+a₂ * a₃ can both be possible valid solutions for a same problem but formulated as different expression sequences or trees. The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating wrong when evaluating a valid expression variant. To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants. We propose a novel non-autoregressive solver, named MWP-NAS, to parse the problem and deduce the solution expression based on the unified tree. For evaluating the possible expression variants, we design a path-based metric to evaluate the partial accuracy of expressions of a unified tree. The results from extensive experiments conducted on Math23K and MAWPS demonstrate the effectiveness of our proposed MWP-NAS. The codes and checkpoints are available at: https://github.com/mengqunhan/MWP-NAS.
%R 10.18653/v1/2023.emnlp-main.199
%U https://aclanthology.org/2023.emnlp-main.199
%U https://doi.org/10.18653/v1/2023.emnlp-main.199
%P 3290-3301

Markdown (Informal)

[Non-Autoregressive Math Word Problem Solver with Unified Tree Structure](https://aclanthology.org/2023.emnlp-main.199) (Bin et al., EMNLP 2023)

• Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (Bin et al., EMNLP 2023)

ACL

• Yi Bin, Mengqun Han, Wenhao Shi, Lei Wang, Yang Yang, See-Kiong Ng, and Heng Shen. 2023. Non-Autoregressive Math Word Problem Solver with Unified Tree Structure. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3290–3301, Singapore. Association for Computational Linguistics.