@inproceedings{abercrombie-etal-2025-consistency,
    title = "Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement",
    author = "Abercrombie, Gavin  and
      Dinkar, Tanvi  and
      Cercas Curry, Amanda  and
      Rieser, Verena  and
      Hovy, Dirk",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Frenda, Simona  and
      Tonelli, Sara  and
      Dudy, Shiran",
    booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nlperspectives-1.6/",
    pages = "63--74",
    ISBN = "979-8-89176-350-0",
    abstract = "We commonly use agreement measures to assess the utility of judgements made by human annotators in Natural Language Processing (NLP) tasks. While inter-annotator agreement is frequently used as an indication of label reliability by measuring consistency between annotators, we argue for the additional use of intra-annotator agreement to measure label stability (and annotator consistency) over time. However, in a systematic review, we find that the latter is rarely reported in this field. Calculating these measures can act as important quality control and could provide insights into why annotators disagree. We conduct exploratory annotation experiments to investigate the relationships between these measures and perceptions of subjectivity and ambiguity in text items, finding that annotators provide inconsistent responses around 25{\%} of the time across four different NLP tasks."
}