@inproceedings{chollampatt-ng-2017-connecting,
    title = "Connecting the Dots: Towards Human-Level Grammatical Error Correction",
    author = "Chollampatt, Shamil  and
      Ng, Hwee Tou",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-5037/",
    doi = "10.18653/v1/W17-5037",
    pages = "327--333",
    abstract = "We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14{\%} F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62{\%} F 0.5 over the best previous published score."
}