@inproceedings{flachs-etal-2020-grammatical,
    title = "Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses",
    author = "Flachs, Simon  and
      Lacroix, Oph{\'e}lie  and
      Yannakoudakis, Helen  and
      Rei, Marek  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.680",
    doi = "10.18653/v1/2020.emnlp-main.680",
    pages = "8467--8478",
    abstract = "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 state-of-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 open-domain GEC models that generalize to different topics and genres.",
}