# Corpora Generation for Grammatical Error Correction

## TL;DR

This paper introduces two novel methods for generating large-scale parallel datasets from Wikipedia data to improve neural grammatical error correction models, achieving state-of-the-art results.

## Contribution

It presents two data augmentation strategies for GEC using Wikipedia, enabling effective training of neural models without extensive manual annotations.

## Key findings

- Both data generation methods produce comparable corpora (~4B tokens).
- Neural GEC models trained on these corpora outperform previous state-of-the-art.
- Ensembling and fine-tuning further enhance model performance.

## Abstract

Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. We describe two approaches for generating large parallel datasets for GEC using publicly available Wikipedia data. The first method extracts source-target pairs from Wikipedia edit histories with minimal filtration heuristics, while the second method introduces noise into Wikipedia sentences via round-trip translation through bridge languages. Both strategies yield similar sized parallel corpora containing around 4B tokens. We employ an iterative decoding strategy that is tailored to the loosely supervised nature of our constructed corpora. We demonstrate that neural GEC models trained using either type of corpora give similar performance. Fine-tuning these models on the Lang-8 corpus and ensembling allows us to surpass the state of the art on both the CoNLL-2014 benchmark and the JFLEG task. We provide systematic analysis that compares the two approaches to data generation and highlights the effectiveness of ensembling.