@inproceedings{wein-schneider-2022-crowdsourcing,
    title = "Crowdsourcing Preposition Sense Disambiguation with High Precision via a Priming Task",
    author = "Wein, Shira  and
      Schneider, Nathan",
    editor = "Dragut, Eduard  and
      Li, Yunyao  and
      Popa, Lucian  and
      Vucetic, Slobodan  and
      Srivastava, Shashank",
    booktitle = "Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.dash-1.3/",
    pages = "15--22",
    abstract = "The careful design of a crowdsourcing protocol is critical to eliciting highly accurate annotations from untrained workers. In this work, we explore the development of crowdsourcing protocols for a challenging word sense disambiguation task. We find that (a) selecting a similar example usage can serve as a proxy for selecting an explicit definition of the sense, and (b) priming workers with an additional, related task within the HIT improves performance on the main proxy task. Ultimately, we demonstrate the usefulness of our crowdsourcing elicitation technique as an effective alternative to previously investigated training strategies, which can be used if agreement on a challenging task is low."
}