@inproceedings{deb-etal-2019-diversifying,
    title = "Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder",
    author = "Deb, Budhaditya  and
      Bailey, Peter  and
      Shokouhi, Milad",
    editor = "Loukina, Anastassia  and
      Morales, Michelle  and
      Kumar, Rohit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/N19-2006/",
    doi = "10.18653/v1/N19-2006",
    pages = "40--47",
    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 {\ensuremath{\sim}}30{\ensuremath{-}}40{\%} without significant impact on relevance. This translated to a {\ensuremath{\sim}}5{\%} gain in click-rate in our online production system."
}