Efficient Natural Language Response Suggestion for Smart Reply

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This paper presents a computationally efficient machine-learned method for natural language response suggestion.

• Feed-forward neural networks using n-gram embedding features encode messages into vectors which are optimized to give message-response pairs a high dot-product value.
• An optimized search finds response suggestions.
• The method is evaluated in a large-scale commercial e-mail application, Inbox by Gmail.