No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models

Anonymous

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.

Paper Type: long

Research Area: Multilinguality and Language Diversity

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English, Estonian, German, Czech

Consent To Share Submission Details: On behalf of all authors, we agree to the terms above to share our submission details.