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        <title>Demographic Features for Annotation-Aware Classification</title>
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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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        <name type="personal">
            <namePart type="given">Maria</namePart>
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            <namePart type="given">Marie</namePart>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>This paper revisits the use of annotator demographics as interpretable meta-information for modeling such variation. We adapt a lightweight attention mechanism, Annotation-Wise Attention Network (AWAN), to condition predictions on demographic features, enabling per-annotator modeling. Experiments on the EXIST sexism dataset show that AWAN improves classification performance over standard baselines, especially in cases of high annotator disagreement.</abstract>
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%0 Conference Proceedings
%T Demographic Features for Annotation-Aware Classification
%A Tahaei, Narjes
%A Bergler, Sabine
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F tahaei-bergler-2025-demographic
%X This paper revisits the use of annotator demographics as interpretable meta-information for modeling such variation. We adapt a lightweight attention mechanism, Annotation-Wise Attention Network (AWAN), to condition predictions on demographic features, enabling per-annotator modeling. Experiments on the EXIST sexism dataset show that AWAN improves classification performance over standard baselines, especially in cases of high annotator disagreement.
%U https://aclanthology.org/2025.ranlp-1.142/
%P 1232-1236

Markdown (Informal)

[Demographic Features for Annotation-Aware Classification](https://aclanthology.org/2025.ranlp-1.142/) (Tahaei & Bergler, RANLP 2025)

• Demographic Features for Annotation-Aware Classification (Tahaei & Bergler, RANLP 2025)

ACL

• Narjes Tahaei and Sabine Bergler. 2025. Demographic Features for Annotation-Aware Classification. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 1232–1236, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.