Multilingual Pre-trained Models for Low-Resource Language Error Correction

Abstract

Abstract: Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, these annotations are unavailable in many low-resource languages. In this paper, we investigate GED in this context. Leveraging the zero-shot cross-lingual transfer capabilities of multilingual pre-trained language models, we train a model using data from a diverse set of languages to generate synthetic errors in other languages. These synthetic error corpora are then used to train a GED model. Specifically we propose a two-stage fine-tuning pipeline where the GED model is first fine-tuned on mult

Research Question

Can multilingual pre-trained models fine-tuned on synthetic error corpora achieve comparable accuracy to supervised baselines on the BEA-2019 shared task benchmark for unseen low-resource languages?

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Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance