- **Flexibility**: One of the limitations of this approach lies in the requirement for a homologous teacher model in terms of paradigm and vocabulary table with the student model. This can hinder the method's flexibility and may pose challenges during the preparation of the teacher model.
- **Scope**: The pre-trained teacher model tends to be already a strong model; do we truly need to train a student model from scratch using weighted training? Is it possible to simply fine-tune the teacher model with weighted training? Weighted training would be meaningful if our goal is to obtain a small yet strong student model. However, the authors have not clearly indicated the specific targeted scenarios in this paper.
- **Experiments**:
1) The authors did not explore how different teacher models affect the student's learning effectiveness, which could have provided valuable insights into the impact of varying teacher models on the proposed method's performance. The choice of teacher model may lack flexibility; however, it is worth noting that there are numerous robust GEC systems that share the same PLM architecture.
2) The effectiveness of the proposed approach for other language families remains unknown.
2) The effectiveness of the proposed approach for other language families remains unknown.