@inproceedings{fei-etal-2023-enhancing,
title = "Enhancing Grammatical Error Correction Systems with Explanations",
author = "Fei, Yuejiao and
Cui, Leyang and
Yang, Sen and
Lam, Wai and
Lan, Zhenzhong and
Shi, Shuming",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.413",
doi = "10.18653/v1/2023.acl-long.413",
pages = "7489--7501",
abstract = "Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of errors (evidence words) and the corresponding error types are two key factors. To enhance GEC systems with explanations, we introduce EXPECT, a large dataset annotated with evidence words and grammatical error types. We propose several baselines and anlysis to understand this task. Furthermore, human evaluation verifies our explainable GEC system{'}s explanations can assist second-language learners in determining whether to accept a correction suggestion and in understanding the associated grammar rule.",
}
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<abstract>Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of errors (evidence words) and the corresponding error types are two key factors. To enhance GEC systems with explanations, we introduce EXPECT, a large dataset annotated with evidence words and grammatical error types. We propose several baselines and anlysis to understand this task. Furthermore, human evaluation verifies our explainable GEC system’s explanations can assist second-language learners in determining whether to accept a correction suggestion and in understanding the associated grammar rule.</abstract>
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%0 Conference Proceedings
%T Enhancing Grammatical Error Correction Systems with Explanations
%A Fei, Yuejiao
%A Cui, Leyang
%A Yang, Sen
%A Lam, Wai
%A Lan, Zhenzhong
%A Shi, Shuming
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F fei-etal-2023-enhancing
%X Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of errors (evidence words) and the corresponding error types are two key factors. To enhance GEC systems with explanations, we introduce EXPECT, a large dataset annotated with evidence words and grammatical error types. We propose several baselines and anlysis to understand this task. Furthermore, human evaluation verifies our explainable GEC system’s explanations can assist second-language learners in determining whether to accept a correction suggestion and in understanding the associated grammar rule.
%R 10.18653/v1/2023.acl-long.413
%U https://aclanthology.org/2023.acl-long.413
%U https://doi.org/10.18653/v1/2023.acl-long.413
%P 7489-7501
Markdown (Informal)
[Enhancing Grammatical Error Correction Systems with Explanations](https://aclanthology.org/2023.acl-long.413) (Fei et al., ACL 2023)
ACL
- Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, and Shuming Shi. 2023. Enhancing Grammatical Error Correction Systems with Explanations. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7489–7501, Toronto, Canada. Association for Computational Linguistics.