Paper Info
Title
Corpora Generation for Grammatical Error Correction.
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.
Year
Venue
Field
2019
arXiv: Computation and Language
Computer science,Error detection and correction,Natural language processing,Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1904.05780
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jared Lichtarge100.68
Christopher Alberti2212.79
Shankar Kumar323220.70
Noam Shazeer4108943.70
Niki Parmar552213.34
Simon Tong6157494.86