Enhancing Grammatical Error Correction Systems with Explanations

Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi


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.
Anthology ID:
2023.acl-long.413
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7489–7501
Language:
URL:
https://aclanthology.org/2023.acl-long.413
DOI:
10.18653/v1/2023.acl-long.413
Bibkey:
Cite (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.
Cite (Informal):
Enhancing Grammatical Error Correction Systems with Explanations (Fei et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.413.pdf
Video:
 https://aclanthology.org/2023.acl-long.413.mp4