Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Article Status
Published
Authors/contributors
- Wang, Zichao (Author)
- Lan, Andrew (Author)
- Baraniuk, Richard (Author)
Title
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Date
2021
Proceedings Title
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Conference Name
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Place
Online and Punta Cana, Dominican Republic
Publisher
Association for Computational Linguistics
Pages
5986-5999
Language
en
Accessed
07/04/2023, 16:35
Library Catalogue
DOI.org (Crossref)
Extra
Citation Key: wang2021c
<标题>: 具有数学一致性和问题情境约束的数学应用题生成
<AI Smry>: A novel MWP generation approach is developed that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improved the mathematical validity of the generatedMWPs.
Citation
Wang, Z., Lan, A., & Baraniuk, R. (2021). Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 5986–5999. https://doi.org/10.18653/v1/2021.emnlp-main.484
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