Jared Lichtarge author Chris Alberti author Shankar Kumar author Noam Shazeer author Niki Parmar author Simon Tong author 2019-06 text Jill Burstein editor Christy Doran editor Thamar Solorio editor Association for Computational Linguistics Minneapolis, Minnesota conference publication 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 ‘14 benchmark and the JFLEG task. We present systematic analysis that compares the two approaches to data generation and highlights the effectiveness of ensembling. lichtarge-etal-2019-corpora 10.18653/v1/N19-1333 https://aclanthology.org/N19-1333 2019-06 3291 3301