Papers with generating corrections

1 papers
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.

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