@inproceedings{homayounirad-etal-2025-will,
    title = "Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments",
    author = "Homayounirad, Amir  and
      Liscio, Enrico  and
      Wang, Tong  and
      Jonker, Catholijn M  and
      Siebert, Luciano Cavalcante",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.824/",
    doi = "10.18653/v1/2025.findings-emnlp.824",
    pages = "15237--15252",
    ISBN = "979-8-89176-335-7",
    abstract = "Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly improves the model performance of flagging subjective arguments. Furthermore, combining contrastive loss with binary cross-entropy loss does not improve performance but reduces the dependency on per-label subjectivity. Our proposed methods can help identify arguments that individuals may interpret differently, fostering a more nuanced annotation process."
}