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arXiv:1606.04870cs.CL

Smart Reply: Automated Response Suggestion for Email

Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, Vivek Ramavajjala

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Abstract

In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning. We describe the architecture of the system as well as the challenges that we faced while building it, like response diversity and scalability. We also introduce a new method for semantic clustering of user-generated content that requires only a modest amount of explicitly labeled data.

arXiv comments
Accepted to KDD 2016
Journal reference
DOI
Subjects
cs.CLComputation and Language

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Feb 2023 to Feb 2023. Found in 1 HN submission from Jun 2016 to Jun 2016. Open submission discussions.

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    • About emails: No for ads. Yes for training models for Smart Reply, from: https://arxiv.org/pdf/1606.04870.pdf Privacy Note that all email data (raw data, preprocessed data and training data) was encrypted. Engineers could only inspect aggregated statistics on anonymized…

      kartayyarView on HNParent context

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