Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection

TL;DR

This paper presents a zero-shot cross-lingual approach using multilingual models to generate synthetic error data for grammatical error detection in low-resource languages, outperforming existing annotation-free methods.

Contribution

It introduces a two-stage fine-tuning pipeline leveraging synthetic data from multilingual models for improved low-resource GED without human annotations.

Findings

Outperforms state-of-the-art annotation-free GED methods

Generates more diverse and human-like errors

Effective in low-resource language settings

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 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.