@inproceedings{desmond-etal-2020-label,
    title = "Label Noise in Context",
    author = "Desmond, Michael  and
      Finegan-Dollak, Catherine  and
      Boston, Jeff  and
      Arnold, Matt",
    editor = "Celikyilmaz, Asli  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-demos.21/",
    doi = "10.18653/v1/2020.acl-demos.21",
    pages = "157--186",
    abstract = "Label noise{---}incorrectly or ambiguously labeled training examples{---}can negatively impact model performance. Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process. Examples incorrectly flagged as noise waste reviewers' time, and correcting label noise without guidance can be difficult. We propose LNIC, a noise-detection method that uses an example`s neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the ex- ample was flagged as noise. We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F0.5-score. We also show how LNIC`s training set context helps a reviewer to understand and correct label noise in a dataset. The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners."
}