Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard · 2020
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications.We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency.Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to stateof-the-art GEC systems.We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains.We hope this work shall facilitate the development of opendomain GEC models that generalize to different topics and genres.