Efficient and Interpretable Grammatical Error Correction with Mixture of Experts

• URL: http://arxiv.org/abs/2410.23507v1
• Date: Wed, 30 Oct 2024 23:27:54 GMT
• Title: Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
• Authors: Muhammad Reza Qorib, Alham Fikri Aji, Hwee Tou Ng,
• Abstract summary: 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.
• Score: 33.748193858033346
• License:
• Abstract: 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.

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