@inproceedings{rozovskaya-roth-2021-good,
    title = "How Good (really) are Grammatical Error Correction Systems?",
    author = "Rozovskaya, Alla  and
      Roth, Dan",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2021.eacl-main.231/",
    doi = "10.18653/v1/2021.eacl-main.231",
    pages = "2686--2698",
    abstract = "Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go. This analysis paper studies the performance of GEC systems relative to closest-gold {--} a gold reference text created relative to the output of a system. Surprisingly, we show that the real performance is 20-40 points better than standard evaluations show. Moreover, the performance remains high even when considering any of the top-10 hypotheses produced by a system. Importantly, the type of mistakes corrected by lower-ranked hypotheses differs in interesting ways from the top one, providing an opportunity to focus on a range of errors {--} local spelling and grammar edits vs. more complex lexical improvements. Our study shows these results in English and Russian, and thus provides a preliminary proposal for a more realistic evaluation of GEC systems."
}