@inproceedings{yuan-bryant-2021-document,
    title = "Document-level grammatical error correction",
    author = "Yuan, Zheng  and
      Bryant, Christopher",
    editor = "Burstein, Jill  and
      Horbach, Andrea  and
      Kochmar, Ekaterina  and
      Laarmann-Quante, Ronja  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Yannakoudakis, Helen  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.bea-1.8/",
    pages = "75--84",
    abstract = "Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set."
}