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.- Anthology ID:
- N19-2006
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Anastassia Loukina, Michelle Morales, Rohit Kumar
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 40–47
- Language:
- URL:
- https://aclanthology.org/N19-2006
- DOI:
- 10.18653/v1/N19-2006
- Cite (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.
- Cite (Informal):
- Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder (Deb et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/N19-2006.pdf