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        <title>Detecting reported speech as a token classification task: an application to Classical Latin?</title>
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            <namePart type="given">Diego</namePart>
            <namePart type="family">Alves</namePart>
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        <name type="personal">
            <namePart type="given">Yuri</namePart>
            <namePart type="family">Bizzoni</namePart>
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        <name type="personal">
            <namePart type="given">Stefania</namePart>
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    <abstract>This paper presents the first application of an automatic token-classification approach for detecting reported speech spans in Classical Latin using transformer-based neural architectures.Focusing on Seneca the Elder’s Declamatory Anthology, the study addresses the text’s highly polyphonic nature, resulting from theuse of reported speech. Instead of relying exclusively on sentence-level syntactic information, the proposed approach treats reported speech detection as a token-level sequence labeling problem. This enables the identification of reported speech spans extending across multiple sentences. We fine-tune three Latin neural language models —LatinBERT, LaBERTa, and PhilBERTa— for binary token-level classification and conduct experiments both with and without punctuation. The results show that RoBERTa-based models effectively identify reported speech, with LaBERTa achieving the best performance (F1 scores above 0.90).</abstract>
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%0 Conference Proceedings
%T Detecting reported speech as a token classification task: an application to Classical Latin?
%A Dei, Agustin
%Y Alves, Diego
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Pagel, Janis
%Y Szpakowicz, Stan
%S Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-373-9
%F dei-2026-detecting-reported
%X This paper presents the first application of an automatic token-classification approach for detecting reported speech spans in Classical Latin using transformer-based neural architectures.Focusing on Seneca the Elder’s Declamatory Anthology, the study addresses the text’s highly polyphonic nature, resulting from theuse of reported speech. Instead of relying exclusively on sentence-level syntactic information, the proposed approach treats reported speech detection as a token-level sequence labeling problem. This enables the identification of reported speech spans extending across multiple sentences. We fine-tune three Latin neural language models —LatinBERT, LaBERTa, and PhilBERTa— for binary token-level classification and conduct experiments both with and without punctuation. The results show that RoBERTa-based models effectively identify reported speech, with LaBERTa achieving the best performance (F1 scores above 0.90).
%U https://aclanthology.org/2026.latechclfl-1.24/
%P 251-256

Markdown (Informal)

[Detecting reported speech as a token classification task: an application to Classical Latin?](https://aclanthology.org/2026.latechclfl-1.24/) (Dei, LaTeCH-CLfL 2026)

• Detecting reported speech as a token classification task: an application to Classical Latin? (Dei, LaTeCH-CLfL 2026)

ACL

• Agustin Dei. 2026. Detecting reported speech as a token classification task: an application to Classical Latin?. In Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026, pages 251–256, Rabat, Morocco. Association for Computational Linguistics.