@inproceedings{li-etal-2020-context-aware,
    title = "Context-aware Stand-alone Neural Spelling Correction",
    author = "Li, Xiangci  and
      Liu, Hairong  and
      Huang, Liang",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.findings-emnlp.37/",
    doi = "10.18653/v1/2020.findings-emnlp.37",
    pages = "407--414",
    abstract = "Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperform the previous state-of-the-art result by 12.8{\%} absolute F0.5 score."
}