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    <titleInfo>
        <title>Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression</title>
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    <name type="personal">
        <namePart type="given">Yuxia</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Karin</namePart>
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    <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.</abstract>
    <identifier type="citekey">wang-etal-2022-uncertainty</identifier>
    <identifier type="doi">10.1162/tacl_a_00483</identifier>
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        <url>https://aclanthology.org/2022.tacl-1.39</url>
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        <date>2022</date>
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            <start>680</start>
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%0 Journal Article
%T Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression
%A Wang, Yuxia
%A Beck, Daniel
%A Baldwin, Timothy
%A Verspoor, Karin
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F wang-etal-2022-uncertainty
%X 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.
%R 10.1162/tacl_a_00483
%U https://aclanthology.org/2022.tacl-1.39
%U https://doi.org/10.1162/tacl_a_00483
%P 680-696

Markdown (Informal)

[Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression](https://aclanthology.org/2022.tacl-1.39) (Wang et al., TACL 2022)

• Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (Wang et al., TACL 2022)

ACL

• Yuxia Wang, Daniel Beck, Timothy Baldwin, and Karin Verspoor. 2022. Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression. Transactions of the Association for Computational Linguistics, 10:680–696.