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        <title>Analyzing Neural Discourse Coherence Models</title>
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        <namePart type="given">Youmna</namePart>
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        <namePart type="given">Josef</namePart>
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    <name type="personal">
        <namePart type="given">Helen</namePart>
        <namePart type="family">Yannakoudakis</namePart>
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    <name type="personal">
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            <title>Proceedings of the First Workshop on Computational Approaches to Discourse</title>
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            <namePart type="given">Chloé</namePart>
            <namePart type="family">Braud</namePart>
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            <namePart type="given">Christian</namePart>
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            <namePart type="given">Junyi</namePart>
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            <namePart type="given">Annie</namePart>
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    <abstract>In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.</abstract>
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%0 Conference Proceedings
%T Analyzing Neural Discourse Coherence Models
%A Farag, Youmna
%A Valvoda, Josef
%A Yannakoudakis, Helen
%A Briscoe, Ted
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%S Proceedings of the First Workshop on Computational Approaches to Discourse
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F farag-etal-2020-analyzing
%X In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.
%R 10.18653/v1/2020.codi-1.11
%U https://aclanthology.org/2020.codi-1.11/
%U https://doi.org/10.18653/v1/2020.codi-1.11
%P 102-112

Markdown (Informal)

[Analyzing Neural Discourse Coherence Models](https://aclanthology.org/2020.codi-1.11/) (Farag et al., CODI 2020)

• Analyzing Neural Discourse Coherence Models (Farag et al., CODI 2020)

ACL

• Youmna Farag, Josef Valvoda, Helen Yannakoudakis, and Ted Briscoe. 2020. Analyzing Neural Discourse Coherence Models. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 102–112, Online. Association for Computational Linguistics.