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    <titleInfo>
        <title>Annotating Coherence Relations for Studying Topic Transitions in Social Talk</title>
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        <namePart type="given">Alex</namePart>
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        <namePart type="given">Sophia</namePart>
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            <title>Proceedings of the 14th Linguistic Annotation Workshop</title>
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            <namePart type="given">Stefanie</namePart>
            <namePart type="family">Dipper</namePart>
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        <name type="personal">
            <namePart type="given">Amir</namePart>
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    <abstract>This study develops the strand of research on topic transitions in social talk which aims to gain a better understanding of interlocutors’ conversational goals. Lưu and Malamud (2020) proposed that one way to identify such transitions is to annotate coherence relations, and then to identify utterances potentially expressing new topics as those that fail to participate in these relations. This work validates and refines their suggested annotation methodology, focusing on annotating most prominent coherence relations in face-to-face social dialogue. The result is a publicly accessible gold standard corpus with efficient and reliable annotation, whose broad coverage provides a foundation for future steps of identifying and classifying new topic utterances.</abstract>
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        <url>https://aclanthology.org/2020.law-1.17</url>
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%0 Conference Proceedings
%T Annotating Coherence Relations for Studying Topic Transitions in Social Talk
%A Luu, Alex
%A Malamud, Sophia A.
%Y Dipper, Stefanie
%Y Zeldes, Amir
%S Proceedings of the 14th Linguistic Annotation Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain
%F luu-malamud-2020-annotating
%X This study develops the strand of research on topic transitions in social talk which aims to gain a better understanding of interlocutors’ conversational goals. Lưu and Malamud (2020) proposed that one way to identify such transitions is to annotate coherence relations, and then to identify utterances potentially expressing new topics as those that fail to participate in these relations. This work validates and refines their suggested annotation methodology, focusing on annotating most prominent coherence relations in face-to-face social dialogue. The result is a publicly accessible gold standard corpus with efficient and reliable annotation, whose broad coverage provides a foundation for future steps of identifying and classifying new topic utterances.
%U https://aclanthology.org/2020.law-1.17
%P 174-179

Markdown (Informal)

[Annotating Coherence Relations for Studying Topic Transitions in Social Talk](https://aclanthology.org/2020.law-1.17) (Luu & Malamud, LAW 2020)

• Annotating Coherence Relations for Studying Topic Transitions in Social Talk (Luu & Malamud, LAW 2020)

ACL

• Alex Luu and Sophia A. Malamud. 2020. Annotating Coherence Relations for Studying Topic Transitions in Social Talk. In Proceedings of the 14th Linguistic Annotation Workshop, pages 174–179, Barcelona, Spain. Association for Computational Linguistics.