@inproceedings{didenko-shaptala-2019-multi,
    title = "Multi-headed Architecture Based on {BERT} for Grammatical Errors Correction",
    author = "Didenko, Bohdan  and
      Shaptala, Julia",
    editor = "Yannakoudakis, Helen  and
      Kochmar, Ekaterina  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Zesch, Torsten",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-4426/",
    doi = "10.18653/v1/W19-4426",
    pages = "246--251",
    abstract = "In this paper, we describe our approach to GEC using the BERT model for creation of encoded representation and some of our enhancements, namely, ``Heads'' are fully-connected networks which are used for finding the errors and later receive recommendation from the networks on dealing with a highlighted part of the sentence only. Among the main advantages of our solution is increasing the system productivity and lowering the time of processing while keeping the high accuracy of GEC results."
}