Arabic Grammatical Error Detection Using Transformers-based Pretrained Language Models

Abstract

This paper presents a new study to use pre-trained language models based on the transformers for Arabic grammatical error detection (GED). We proposed fine-tuned language models based on pre-trained language models called AraBERT and M-BERT to perform Arabic GED on two approaches, which are the token level and sentence level. Fine-tuning was done with different publicly available Arabic datasets. The proposed models outperform similar studies with F1 value of 0.87, recall of 0.90, precision of 0.83 at the token level, and F1 of 0.98, recall of 0.99, and precision of 0.97 at the sentence level. Whereas the other studies in the same field (i.e., GED) results less than the current study (e.g., F0.5 of 69.21). Moreover, the current study shows that the fine-tuned language models that were built on the monolingual pre-trained language models result in better performance than the multilingual pre-trained language models in Arabic.