Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection
This addresses the lack of annotated data for grammatical error detection in low-resource languages, though it is incremental as it builds on existing multilingual models.
The paper tackles the problem of grammatical error detection in low-resource languages by using zero-shot cross-lingual transfer to generate synthetic error corpora, which outperforms current state-of-the-art annotation-free methods and produces errors more similar to human errors.
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 multilingual synthetic data from target languages followed by fine-tuning on human-annotated GED corpora from source languages. This approach outperforms current state-of-the-art annotation-free GED methods. We also analyse the errors produced by our method and other strong baselines, finding that our approach produces errors that are more diverse and more similar to human errors.