<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cui-2025-xiacui">
    <titleInfo>
        <title>xiacui at SemEval-2025 Task 11: Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xia</namePart>
        <namePart type="family">Cui</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Rosenthal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aiala</namePart>
            <namePart type="family">Rosá</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vienna, Austria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-273-2</identifier>
    </relatedItem>
    <abstract>This paper explores the application of a simplified weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accuracy, and Jaccard similarity coefficients. The results demonstrate that the weighted loss function improves performance on high-frequency emotion classes but shows limited impact on minority classes. These findings underscore both the effectiveness and the challenges of applying this approach to imbalanced multi-label emotion detection.</abstract>
    <identifier type="citekey">cui-2025-xiacui</identifier>
    <location>
        <url>https://aclanthology.org/2025.semeval-1.37/</url>
    </location>
    <part>
        <date>2025-07</date>
        <extent unit="page">
            <start>247</start>
            <end>256</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T xiacui at SemEval-2025 Task 11: Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
%A Cui, Xia
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F cui-2025-xiacui
%X This paper explores the application of a simplified weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accuracy, and Jaccard similarity coefficients. The results demonstrate that the weighted loss function improves performance on high-frequency emotion classes but shows limited impact on minority classes. These findings underscore both the effectiveness and the challenges of applying this approach to imbalanced multi-label emotion detection.
%U https://aclanthology.org/2025.semeval-1.37/
%P 247-256

Markdown (Informal)

[xiacui at SemEval-2025 Task 11: Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss](https://aclanthology.org/2025.semeval-1.37/) (Cui, SemEval 2025)

• xiacui at SemEval-2025 Task 11: Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss (Cui, SemEval 2025)

ACL

• Xia Cui. 2025. xiacui at SemEval-2025 Task 11: Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 247–256, Vienna, Austria. Association for Computational Linguistics.