@article{wang-etal-2022-uncertainty,
    title = "Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression",
    author = "Wang, Yuxia  and
      Beck, Daniel  and
      Baldwin, Timothy  and
      Verspoor, Karin",
    editor = "Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "10",
    year = "2022",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.tacl-1.39/",
    doi = "10.1162/tacl_a_00483",
    pages = "680--696",
    abstract = "State-of-the-art classification and regression models are often not well calibrated, and cannot reliably provide uncertainty estimates, limiting their utility in safety-critical applications such as clinical decision-making. While recent work has focused on calibration of classifiers, there is almost no work in NLP on calibration in a regression setting. In this paper, we quantify the calibration of pre- trained language models for text regression, both intrinsically and extrinsically. We further apply uncertainty estimates to augment training data in low-resource domains. Our experiments on three regression tasks in both self-training and active-learning settings show that uncertainty estimation can be used to increase overall performance and enhance model generalization."
}