No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models
Agnes Luhtaru, Elizaveta Korotkova, Mark Fishel
Main: Multilinguality and Language Diversity 2 Oral Paper
Session 8: Multilinguality and Language Diversity 2 (Oral)
Conference Room: Marie Louise 1
Conference Time: March 19, 16:00-17:30 (CET) (Europe/Malta)
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Abstract:
Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English. We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction. We show that MT models are not only capable of error correction out-of-the-box, but that they can also be fine-tuned to even better correction quality. Results show the effectiveness of this approach, with our multilingual model outperforming similar-sized mT5-based models and even competing favourably with larger models.