An Empirical Investigation of Domain Adaptation Ability for Chinese
  Spelling Check Models
Xi Wang Ruoqing Zhao Hongliang Dai Piji Li
Chinese Spelling Check (CSC) is a meaningful task in the area of Natural
Language Processing (NLP) which aims at detecting spelling errors in Chinese
texts and then correcting these errors. However, CSC models are based on
pretrained language models, which are trained on a general corpus.
Consequently, their performance may drop when confronted with downstream tasks
involving domain-specific terms. In this paper, we conduct a thorough
evaluation about the domain adaption ability of various typical CSC models by
building three new datasets encompassing rich domain-specific terms from the
financial, medical, and legal domains. Then we conduct empirical investigations
in the corresponding domain-specific test datasets to ascertain the
cross-domain adaptation ability of several typical CSC models. We also test the
performance of the popular large language model ChatGPT. As shown in our
experiments, the performances of the CSC models drop significantly in the new
domains.