@inproceedings{deb2019diversifying,
author = {Deb, Budhaditya and Bailey, Peter and Shokouhi, Milad},
title = {Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder},
booktitle = {NAACL-HLT 2019},
year = {2019},
month = {June},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/diversifying-reply-suggestions-using-a-matching-conditional-variational-autoencoder/},
pages = {40-47},
}