  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.
