Efficient Natural Language Response Suggestion for Smart Reply

Matthew Henderson; Rami Al-Rfou; Brian Strope; Yun-hsuan Sung; Laszlo Lukacs; Ruiqi Guo; Sanjiv Kumar; Balint Miklos; Ray Kurzweil

arXiv · 1705.00652

Efficient Natural Language Response Suggestion for Smart Reply

Matthew Henderson·Rami Al-Rfou·Brian Strope·Yun-hsuan Sung·Laszlo Lukacs·Ruiqi Guo·Sanjiv Kumar·Balint Miklos·Ray Kurzweil

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Abstract

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.

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BibTeX RIS

Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil. 2017-05-01. Efficient Natural Language Response Suggestion for Smart Reply. https://arxiv.org/abs/1705.00652

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