Papers with base systems
Efficient and Interpretable Grammatical Error Correction with Mixture of Experts (2024.findings-emnlp)
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| Challenge: | Error type information has been widely used to improve the performance of grammatical error correction models. |
| Approach: | They propose a mixture-of-experts model for grammatical error correction that uses error type information to generate corrections and combine models. |
| Outcome: | The proposed model achieves the performance of T5-XL with three times fewer effective parameters and produces interpretable corrections by also identifying the error type during inference. |