Towards Minimal Supervision BERT-based Grammar Error Correction
This work addresses the need for more efficient GEC models that require less annotated data, benefiting multilingual applications, though it appears incremental in its approach.
The paper tackled the problem of grammatical error correction (GEC) in data-limited settings by incorporating contextual information from pre-trained language models like BERT, showing strong potential with results indicating improved performance.
Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.