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        <title>Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity</title>
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    <name type="personal">
        <namePart type="given">Xinnuo</namePart>
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    <name type="personal">
        <namePart type="given">Ondřej</namePart>
        <namePart type="family">Dušek</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Verena</namePart>
        <namePart type="family">Rieser</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1432</identifier>
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%0 Conference Proceedings
%T Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity
%A Xu, Xinnuo
%A Dušek, Ondřej
%A Konstas, Ioannis
%A Rieser, Verena
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-better
%X We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.
%R 10.18653/v1/D18-1432
%U https://aclanthology.org/D18-1432
%U https://doi.org/10.18653/v1/D18-1432
%P 3981-3991

Markdown (Informal)

[Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity](https://aclanthology.org/D18-1432) (Xu et al., EMNLP 2018)

• Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (Xu et al., EMNLP 2018)

ACL

• Xinnuo Xu, Ondřej Dušek, Ioannis Konstas, and Verena Rieser. 2018. Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3981–3991, Brussels, Belgium. Association for Computational Linguistics.