Efficient Natural Language Response Suggestion for Smart Reply

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.

• The paper introduces an efficient approach for generating natural language responses in smart reply systems, reducing response times significantly.
• It employs optimized machine learning models that balance accuracy with computational efficiency to enhance user experience.
• Experimental evaluations demonstrate improved performance metrics, validating its viability for large-scale, real-world applications.

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