Impact of Source Language Diversity on Robust Synthetic Error Generation for Low-Resource Grammatical Error Detection

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 goal: To what extent does increasing the diversity of source languages in zero-shot cross-lingual transfer improve the robustness of synthetic error generation for low-resource target languages on standard GED metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.