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        <title>Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder</title>
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        <namePart type="given">Budhaditya</namePart>
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    <name type="personal">
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            <namePart type="given">Michelle</namePart>
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    <abstract>We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based information retrieval architecture, which consists of two parallel encoders to project messages and replies into a common feature representation. During inference, we select replies from a fixed response set using nearest neighbors in the feature space. To diversify responses, we formulate the model as a generative latent variable model with Conditional Variational Auto-Encoder (M-CVAE). We propose a constrained-sampling approach to make the variational inference in M-CVAE efficient for our production system. In offline experiments, M-CVAE consistently increased diversity by ∼30−40% without significant impact on relevance. This translated to a ∼5% gain in click-rate in our online production system.</abstract>
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    <identifier type="doi">10.18653/v1/N19-2006</identifier>
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%0 Conference Proceedings
%T Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder
%A Deb, Budhaditya
%A Bailey, Peter
%A Shokouhi, Milad
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F deb-etal-2019-diversifying
%X We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based information retrieval architecture, which consists of two parallel encoders to project messages and replies into a common feature representation. During inference, we select replies from a fixed response set using nearest neighbors in the feature space. To diversify responses, we formulate the model as a generative latent variable model with Conditional Variational Auto-Encoder (M-CVAE). We propose a constrained-sampling approach to make the variational inference in M-CVAE efficient for our production system. In offline experiments, M-CVAE consistently increased diversity by ∼30−40% without significant impact on relevance. This translated to a ∼5% gain in click-rate in our online production system.
%R 10.18653/v1/N19-2006
%U https://aclanthology.org/N19-2006
%U https://doi.org/10.18653/v1/N19-2006
%P 40-47

Markdown (Informal)

[Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder](https://aclanthology.org/N19-2006) (Deb et al., NAACL 2019)

• Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder (Deb et al., NAACL 2019)

ACL

• Budhaditya Deb, Peter Bailey, and Milad Shokouhi. 2019. Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 40–47, Minneapolis, Minnesota. Association for Computational Linguistics.