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        <title>Is this Dialogue Coherent? Learning from Dialogue Acts and Entities</title>
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            <title>Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
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            <namePart type="given">Olivier</namePart>
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    <abstract>In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.</abstract>
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%0 Conference Proceedings
%T Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
%A Cervone, Alessandra
%A Riccardi, Giuseppe
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F cervone-riccardi-2020-dialogue
%X In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
%R 10.18653/v1/2020.sigdial-1.21
%U https://aclanthology.org/2020.sigdial-1.21
%U https://doi.org/10.18653/v1/2020.sigdial-1.21
%P 162-174

Markdown (Informal)

[Is this Dialogue Coherent? Learning from Dialogue Acts and Entities](https://aclanthology.org/2020.sigdial-1.21) (Cervone & Riccardi, SIGDIAL 2020)

• Is this Dialogue Coherent? Learning from Dialogue Acts and Entities (Cervone & Riccardi, SIGDIAL 2020)

ACL

• Alessandra Cervone and Giuseppe Riccardi. 2020. Is this Dialogue Coherent? Learning from Dialogue Acts and Entities. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 162–174, 1st virtual meeting. Association for Computational Linguistics.