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
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
Publisher
Association for Computational Linguistics
Place
Online and Punta Cana, Dominican Republic
Date
2021
Pages
5986-5999
Citation Key
wang2021c
Accessed
07/04/2023, 16:35
Language
en
Library Catalogue
DOI.org (Crossref)
Extra
<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.
Read_Status: New
Read_Status_Date: 2026-01-26T11:33:49.150Z
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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