@inproceedings{sun-etal-2020-improving,
    title = "Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution",
    author = "Sun, David Q.  and
      Kotek, Hadas  and
      Klein, Christopher  and
      Gupta, Mayank  and
      Li, William  and
      Williams, Jason D.",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.coling-main.316/",
    doi = "10.18653/v1/2020.coling-main.316",
    pages = "3547--3557",
    abstract = "This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30{\%} in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies. This makes DACR not only more accurate but also available to a broad range of labeling tasks. In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies."
}