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.

Training neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrasebased models.Policy gradient training enables direct optimization for exact matches, and while the small datasets in historical text normalization are prohibitive of from-scratch reinforcement learning, we show that policy gradient fine-tuning leads to significant improvements across the board.Policy gradient training, in particular, leads to more accurate normalizations for long or unseen words.