@inproceedings{li-etal-2019-laix,
    title = "The {LAIX} Systems in the {BEA}-2019 {GEC} Shared Task",
    author = "Li, Ruobing  and
      Wang, Chuan  and
      Zha, Yefei  and
      Yu, Yonghong  and
      Guo, Shiman  and
      Wang, Qiang  and
      Liu, Yang  and
      Lin, Hui",
    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://aclanthology.org/W19-4416",
    doi = "10.18653/v1/W19-4416",
    pages = "159--167",
    abstract = "In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.",
}