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    <abstract>Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens. Expression-level methods generate each expression one by one. However, each expression represents a solving step, and there naturally exist parallel or dependent relations between these steps, which are ignored by current sequential methods. Therefore, we integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others. Besides, a bipartite matching algorithm is adopted to align multiple predictions with annotations for each layer. Experiments show our method outperforms other baselines, especially for these equations with complex structures.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.29</identifier>
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%0 Conference Proceedings
%T An Expression Tree Decoding Strategy for Mathematical Equation Generation
%A Zhang, Wenqi
%A Shen, Yongliang
%A Nong, Qingpeng
%A Tan, Zeqi
%A Ma, Yanna
%A Lu, Weiming
%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 zhang-etal-2023-expression
%X Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens. Expression-level methods generate each expression one by one. However, each expression represents a solving step, and there naturally exist parallel or dependent relations between these steps, which are ignored by current sequential methods. Therefore, we integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others. Besides, a bipartite matching algorithm is adopted to align multiple predictions with annotations for each layer. Experiments show our method outperforms other baselines, especially for these equations with complex structures.
%R 10.18653/v1/2023.emnlp-main.29
%U https://aclanthology.org/2023.emnlp-main.29
%U https://doi.org/10.18653/v1/2023.emnlp-main.29
%P 439-456

Markdown (Informal)

[An Expression Tree Decoding Strategy for Mathematical Equation Generation](https://aclanthology.org/2023.emnlp-main.29) (Zhang et al., EMNLP 2023)

• An Expression Tree Decoding Strategy for Mathematical Equation Generation (Zhang et al., EMNLP 2023)

ACL

• Wenqi Zhang, Yongliang Shen, Qingpeng Nong, Zeqi Tan, Yanna Ma, and Weiming Lu. 2023. An Expression Tree Decoding Strategy for Mathematical Equation Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 439–456, Singapore. Association for Computational Linguistics.