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        <title>Discourse Coherence in the Wild: A Dataset, Evaluation and Methods</title>
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        <namePart type="given">Alice</namePart>
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            <title>Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue</title>
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        <name type="personal">
            <namePart type="given">Kazunori</namePart>
            <namePart type="family">Komatani</namePart>
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        <name type="personal">
            <namePart type="given">Diane</namePart>
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        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">Yu</namePart>
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            <namePart type="given">Mikio</namePart>
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    <abstract>To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.</abstract>
    <identifier type="citekey">lai-tetreault-2018-discourse</identifier>
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%0 Conference Proceedings
%T Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
%A Lai, Alice
%A Tetreault, Joel
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F lai-tetreault-2018-discourse
%X To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.
%R 10.18653/v1/W18-5023
%U https://aclanthology.org/W18-5023
%U https://doi.org/10.18653/v1/W18-5023
%P 214-223

Markdown (Informal)

[Discourse Coherence in the Wild: A Dataset, Evaluation and Methods](https://aclanthology.org/W18-5023) (Lai & Tetreault, SIGDIAL 2018)

• Discourse Coherence in the Wild: A Dataset, Evaluation and Methods (Lai & Tetreault, SIGDIAL 2018)

ACL

• Alice Lai and Joel Tetreault. 2018. Discourse Coherence in the Wild: A Dataset, Evaluation and Methods. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 214–223, Melbourne, Australia. Association for Computational Linguistics.