Grammatical error correction (GEC) is a challenging task of natural language
processing techniques. While more attempts are being made in this approach for
universal languages like English or Chinese, relatively little work has been
done for low-resource languages for the lack of large annotated corpora. In
low-resource languages, the current unsupervised GEC based on language model
scoring performs well. However, the pre-trained language model is still to be
explored in this context. This study proposes a BERT-based unsupervised GEC
framework, where GEC is viewed as multi-class classification task. The
framework contains three modules: data flow construction module, sentence
perplexity scoring module, and error detecting and correcting module. We
propose a novel scoring method for pseudo-perplexity to evaluate a sentence's
probable correctness and construct a Tagalog corpus for Tagalog GEC research.
It obtains competitive performance on the Tagalog corpus we construct and
open-source Indonesian corpus and it demonstrates that our framework is
complementary to baseline method for low-resource GEC task.

本文提出了一种基于 BERT 的无监督语法错误校正框架，该框架将语法错误校正视为多类别分类任务，涵盖数据流构建模块、句子困境评分模块和误差检测和校正模块，并提出了一种新的伪困境评分方法来评估句子的可信度，并构建了 Tagalog 语料库进行研究，其表现竞争性较强，证明了该框架对于低资源语法错误校正任务很有用处。