<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="petersen-spalek-2025-german">
    <titleInfo>
        <title>A German WSC dataset comparing coreference resolution by humans and machines</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Wiebke</namePart>
        <namePart type="family">Petersen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Katharina</namePart>
        <namePart type="family">Spalek</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th International Conference on Computational Semantics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kilian</namePart>
            <namePart type="family">Evang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Laura</namePart>
            <namePart type="family">Kallmeyer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sylvain</namePart>
            <namePart type="family">Pogodalla</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Düsseldorf, Germany</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-316-6</identifier>
    </relatedItem>
    <abstract>We present a novel German Winograd-style dataset for direct comparison of human and model behavior in coreference resolution. Ten participants per item provided accuracy, confidence ratings, and response times. Unlike classic WSC tasks, humans select among three pronouns rather than between two potential antecedents, increasing task difficulty. While majority vote accuracy is high, individual responses reveal that not all items are trivial and that variability is obscured by aggregation. Pretrained language models evaluated without fine-tuning show clear performance gaps, yet their accuracy and confidence scores correlate notably with human data, mirroring certain patterns of human uncertainty and error. Dataset-specific limitations, including pragmatic reinterpretations and imbalanced pronoun distributions, highlight the importance of high-quality, balanced resources for advancing computational and cognitive models of coreference resolution.</abstract>
    <identifier type="citekey">petersen-spalek-2025-german</identifier>
    <location>
        <url>https://aclanthology.org/2025.iwcs-main.10/</url>
    </location>
    <part>
        <date>2025-09</date>
        <extent unit="page">
            <start>110</start>
            <end>117</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A German WSC dataset comparing coreference resolution by humans and machines
%A Petersen, Wiebke
%A Spalek, Katharina
%Y Evang, Kilian
%Y Kallmeyer, Laura
%Y Pogodalla, Sylvain
%S Proceedings of the 16th International Conference on Computational Semantics
%D 2025
%8 September
%I Association for Computational Linguistics
%C Düsseldorf, Germany
%@ 979-8-89176-316-6
%F petersen-spalek-2025-german
%X We present a novel German Winograd-style dataset for direct comparison of human and model behavior in coreference resolution. Ten participants per item provided accuracy, confidence ratings, and response times. Unlike classic WSC tasks, humans select among three pronouns rather than between two potential antecedents, increasing task difficulty. While majority vote accuracy is high, individual responses reveal that not all items are trivial and that variability is obscured by aggregation. Pretrained language models evaluated without fine-tuning show clear performance gaps, yet their accuracy and confidence scores correlate notably with human data, mirroring certain patterns of human uncertainty and error. Dataset-specific limitations, including pragmatic reinterpretations and imbalanced pronoun distributions, highlight the importance of high-quality, balanced resources for advancing computational and cognitive models of coreference resolution.
%U https://aclanthology.org/2025.iwcs-main.10/
%P 110-117

Markdown (Informal)

[A German WSC dataset comparing coreference resolution by humans and machines](https://aclanthology.org/2025.iwcs-main.10/) (Petersen & Spalek, IWCS 2025)

• A German WSC dataset comparing coreference resolution by humans and machines (Petersen & Spalek, IWCS 2025)

ACL

• Wiebke Petersen and Katharina Spalek. 2025. A German WSC dataset comparing coreference resolution by humans and machines. In Proceedings of the 16th International Conference on Computational Semantics, pages 110–117, Düsseldorf, Germany. Association for Computational Linguistics.