Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder
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.
Keywords
Cite
@article{arxiv.1903.10630,
title = {Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder},
author = {Budhaditya Deb and Peter Bailey and Milad Shokouhi},
journal= {arXiv preprint arXiv:1903.10630},
year = {2019}
}