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.