@inproceedings{schlechtweg-etal-2025-comedi,
    title = "{C}o{M}e{D}i Shared Task: Median Judgment Classification {\&} Mean Disagreement Ranking with Ordinal Word-in-Context Judgments",
    author = "Schlechtweg, Dominik  and
      Choppa, Tejaswi  and
      Zhao, Wei  and
      Roth, Michael",
    editor = "Roth, Michael  and
      Schlechtweg, Dominik",
    booktitle = "Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.comedi-1.4/",
    pages = "33--47",
    abstract = "We asked task participants to solve two subtasks given a pair of word usages: Ordinal Graded Word-in-Context Classification (OGWiC) and Disagreement in Word-in-Context Ranking (DisWiC). The tasks take a different view on modeling of word meaning by (i) treating WiC as an ordinal classification task, and (ii) making disagreement the explicit detection aim (instead of removing it). OGWiC is solved with relatively high performance while DisWiC proves to be a challenging task. In both tasks, the dominating model architecture uses independently optimized binary Word-in-Context models."
}