Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that have complementary strengths in correcting different error types is very effective in producing better corrections. However, system combination incurs a high computational cost due to the need to run inference on the base systems before running the combination method itself. Therefore, it would be more efficient to have a single model with multiple sub-networks that specialize in correcting different error types. In this paper, we propose a mixture-of-experts model, MoECE, for grammatical error correction. Our model successfully achieves the performance of T5-XL with three times fewer effective parameters. Additionally, our model produces interpretable corrections by also identifying the error type during inference.
@article{arxiv.2410.23507,
title = {Efficient and Interpretable Grammatical Error Correction with Mixture of Experts},
author = {Muhammad Reza Qorib and Alham Fikri Aji and Hwee Tou Ng},
journal= {arXiv preprint arXiv:2410.23507},
year = {2024}
}