@inproceedings{Yitao-etal-2023-grammatical
    author = {Yitao, Liu and Mark, Dras},
    title = {Grammatical Error Correction based on Domain Adaptation},
    booktitle = {"Findings of the Proceedings of the 22nd China National Conference on Computational Linguistics"},
    month = {"August"},
    year = {"2023"},
    address = {"Harbin, China"},
    publisher = {"Chinese Information Processing Society of China"},
    url = {https://aclanthology.org/2023.findings-ccl-1.12},
    pages = {118--129},
    abstract = {"A common issue for grammatical error correction (GEC) is how to combine the native corpus andthe corpus from English as Second Language (ESL) learners together to train the GEC model.For example, though can be trained by the native corpus only, a GEC classifier performed betterwhen trained by the ESL corpus. However, due to the small quantity of the ESL corpus, the nativecorpus needs to be utilized as well to solve the data-limitation problem. Unlike some previousworks which combined them in specific ways or using specific classifiers, we consider this asa domain adaptation problem and provide a common method. It is based on FRUSTRATINGLYEASY DOMAIN ADAPTATION (Daum´e III, 2007), which augments the feature vectors directly toimprove the classifier. We examine this method for correcting article errors along with a numberof baseline systems, and prove that it performs effectively when using appropriate classifiers."},
    language = "English",
}