Grammatical Error Correction with Contrastive Learning in Low Error Density Domains
Hannan Cao, Wenmian Yang, Hwee Tou Ng
Abstract
Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.
BibTeX
@inproceedings{cao-etal-2021-grammatical-error,
title = "Grammatical Error Correction with Contrastive Learning in Low Error Density Domains",
author = "Cao, Hannan and
Yang, Wenmian and
Ng, Hwee Tou",
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.419/",
doi = "10.18653/v1/2021.findings-emnlp.419",
pages = "4867--4874"
}