@inproceedings{weerasooriya-etal-2023-disagreement,
    title = "Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with {D}is{C}o",
    author = "Weerasooriya, Tharindu Cyril  and
      Ororbia, Alexander  and
      Bhensadadia, Raj  and
      KhudaBukhsh, Ashiqur  and
      Homan, Christopher",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.287",
    doi = "10.18653/v1/2023.findings-acl.287",
    pages = "4679--4695",
    abstract = "Annotator disagreement is common whenever human judgment is needed for supervised learning. It is conventional to assume that one label per item represents ground truth. However, this obscures minority opinions, if present. We regard {``}ground truth{''} as the distribution of all labels that a population of annotators could produce, if asked (and of which we only have a small sample). We next introduce DisCo (Distribution from Context), a simple neural model that learns to predict this distribution. The model takes annotator-item pairs, rather than items alone, as input, and performs inference by aggregating over all annotators. Despite its simplicity, our experiments show that, on six benchmark datasets, our model is competitive with, and frequently outperforms, other, more complex models that either do not model specific annotators or were not designed for label distribution learning.",
}

Annotator disagreement is common whenever human judgment is needed for supervised learning. It is conventional to assume that one label per item represents ground truth. However, this obscures minority opinions, if present. We regard {``}ground truth{''} as the distribution of all labels that a population of annotators could produce, if asked (and of which we only have a small sample). We next introduce DisCo (Distribution from Context), a simple neural model that learns to predict this distribution. The model takes annotator-item pairs, rather than items alone, as input, and performs inference by aggregating over all annotators. Despite its simplicity, our experiments show that, on six benchmark datasets, our model is competitive with, and frequently outperforms, other, more complex models that either do not model specific annotators or were not designed for label distribution learning.
Weerasooriya, Tharindu Cyril Ororbia, Alex er Bhensadadia, Raj KhudaBukhsh, Ashiqur Homan, Christopher
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo