Paper page - Efficient Natural Language Response Suggestion for Smart Reply (original) (raw)

Abstract

A machine-learned method using feed-forward neural networks with n-gram embeddings efficiently generates natural language response suggestions by optimizing dot-product values between messages and responses.

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. Compared to a sequence-to-sequence approach, the new system achieves the same quality at a small fraction of the computational requirements and latency.

View arXiv page View PDF Add to collection