@inproceedings{sarumi-etal-2026-fine,
    title = "Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales",
    author = "Sarumi, Olufunke O.  and
      Welch, Charles  and
      Braun, Daniel",
    editor = "Dudy, Shiran  and
      Abercrombie, Gavin  and
      Basile, Valerio  and
      Leonardelli, Elisa  and
      Frenda, Simona",
    booktitle = "Proceedings of the the fifth edition of {NLP}erspectives",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.nlperspectives-1.7/",
    doi = "10.63317/4s7kqwvy5i6x",
    pages = "66--75",
    abstract = "Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators' provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, we condition predictions on both annotator identity and demographic metadata through a representation-level User Passport mechanism. We further introduce two explainer architectures: a post-hoc prompt-based explainer and a prefixed bridge explainer that transfers annotator-conditioned classifier representations directly into a generative model. This design enables explanation generation aligned with individual annotator perspectives. Our results show that incorporating explanation modeling substantially improves predictive performance over a baseline annotator-aware classifier, with the prefixed bridge approach achieving more stable label alignment and higher semantic consistency, while the post-hoc approach yields stronger lexical similarity. These findings indicate that modeling explanations as expressions of fine-grained perspective provides a richer and more faithful representation of disagreement. The proposed approaches advance perspectivist modeling by integrating annotator-specific rationales into both predictive and generative components."
}