<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dawkins-nejadgholi-2022-region">
    <titleInfo>
        <title>Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hillary</namePart>
        <namePart type="family">Dawkins</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Isar</namePart>
        <namePart type="family">Nejadgholi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Certainty calibration is an important goal on the path to interpretability and trustworthy AI. Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential. In the presence of a dominant high-certainty class, for instance the non-entity class in NER problems, existing calibration error measures are completely insensitive to potentially large errors in this certainty region of interest. We introduce a region-balanced calibration error metric that weights all certainty regions equally. When low and mid certainty estimates are taken into account, calibration error is typically larger than previously reported. We introduce a simple extension of temperature scaling, requiring no additional computation, that can reduce both traditional and region-balanced notions of calibration error over existing baselines.</abstract>
    <identifier type="citekey">dawkins-nejadgholi-2022-region</identifier>
    <identifier type="doi">10.18653/v1/2022.acl-short.59</identifier>
    <location>
        <url>https://aclanthology.org/2022.acl-short.59</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>538</start>
            <end>544</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification
%A Dawkins, Hillary
%A Nejadgholi, Isar
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F dawkins-nejadgholi-2022-region
%X Certainty calibration is an important goal on the path to interpretability and trustworthy AI. Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential. In the presence of a dominant high-certainty class, for instance the non-entity class in NER problems, existing calibration error measures are completely insensitive to potentially large errors in this certainty region of interest. We introduce a region-balanced calibration error metric that weights all certainty regions equally. When low and mid certainty estimates are taken into account, calibration error is typically larger than previously reported. We introduce a simple extension of temperature scaling, requiring no additional computation, that can reduce both traditional and region-balanced notions of calibration error over existing baselines.
%R 10.18653/v1/2022.acl-short.59
%U https://aclanthology.org/2022.acl-short.59
%U https://doi.org/10.18653/v1/2022.acl-short.59
%P 538-544

Markdown (Informal)

[Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification](https://aclanthology.org/2022.acl-short.59) (Dawkins & Nejadgholi, ACL 2022)

• Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification (Dawkins & Nejadgholi, ACL 2022)

ACL

• Hillary Dawkins and Isar Nejadgholi. 2022. Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 538–544, Dublin, Ireland. Association for Computational Linguistics.