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    <titleInfo>
        <title>Automatic Annotation of Direct Speech in Written French Narratives</title>
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    <name type="personal">
        <namePart type="given">Noé</namePart>
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    <name type="personal">
        <namePart type="given">Viet</namePart>
        <namePart type="given">Anh</namePart>
        <namePart type="family">Tran</namePart>
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        <namePart type="family">Michel</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Epure</namePart>
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            <namePart type="given">Anna</namePart>
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    <abstract>The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.</abstract>
    <identifier type="citekey">durandard-etal-2023-automatic</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-long.393</identifier>
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        <date>2023-07</date>
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            <start>7129</start>
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%0 Conference Proceedings
%T Automatic Annotation of Direct Speech in Written French Narratives
%A Durandard, Noé
%A Tran, Viet Anh
%A Michel, Gaspard
%A Epure, Elena
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F durandard-etal-2023-automatic
%X The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.
%R 10.18653/v1/2023.acl-long.393
%U https://aclanthology.org/2023.acl-long.393
%U https://doi.org/10.18653/v1/2023.acl-long.393
%P 7129-7147

Markdown (Informal)

[Automatic Annotation of Direct Speech in Written French Narratives](https://aclanthology.org/2023.acl-long.393) (Durandard et al., ACL 2023)

• Automatic Annotation of Direct Speech in Written French Narratives (Durandard et al., ACL 2023)

ACL

• Noé Durandard, Viet Anh Tran, Gaspard Michel, and Elena Epure. 2023. Automatic Annotation of Direct Speech in Written French Narratives. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7129–7147, Toronto, Canada. Association for Computational Linguistics.