@inproceedings{shen-etal-2021-generate-rank,
title = "Generate {\&} Rank: A Multi-task Framework for Math Word Problems",
author = "Shen, Jianhao and
Yin, Yichun and
Li, Lin and
Shang, Lifeng and
Jiang, Xin and
Zhang, Ming and
Liu, Qun",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.195",
doi = "10.18653/v1/2021.findings-emnlp.195",
pages = "2269--2279",
abstract = "Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate {\&} Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7{\%} (78.4{\%} to 85.4{\%}) higher than the state-of-the-art. Code could be found at \url{https://github.com/huawei-noah/noah-research}.",
}
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<abstract>Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% to 85.4%) higher than the state-of-the-art. Code could be found at https://github.com/huawei-noah/noah-research.</abstract>
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%0 Conference Proceedings
%T Generate & Rank: A Multi-task Framework for Math Word Problems
%A Shen, Jianhao
%A Yin, Yichun
%A Li, Lin
%A Shang, Lifeng
%A Jiang, Xin
%A Zhang, Ming
%A Liu, Qun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F shen-etal-2021-generate-rank
%X Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% to 85.4%) higher than the state-of-the-art. Code could be found at https://github.com/huawei-noah/noah-research.
%R 10.18653/v1/2021.findings-emnlp.195
%U https://aclanthology.org/2021.findings-emnlp.195
%U https://doi.org/10.18653/v1/2021.findings-emnlp.195
%P 2269-2279
Markdown (Informal)
[Generate & Rank: A Multi-task Framework for Math Word Problems](https://aclanthology.org/2021.findings-emnlp.195) (Shen et al., Findings 2021)
ACL
- Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021. Generate & Rank: A Multi-task Framework for Math Word Problems. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2269–2279, Punta Cana, Dominican Republic. Association for Computational Linguistics.