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In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. In the design and deployment of such a large-scale and complicated system, we faced several challenges including model selection, performance evaluation, serving and other practical issues. At the core of Smart Compose is a large-scale neural language model. We leveraged state-of-the-art machine learning techniques for language model training which enabled high-quality suggestion prediction, and constructed novel serving infrastructure for high-throughput and real-time inference. Experimental results show the effectiveness of our proposed system design and deployment approach. This system is currently being served in Gmail.
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Mia Xu Chen
Google (United States)
Benjamin N. Lee
Google (United States)
Gagan Bansal
Graphic Era University
Google (United States)
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Chen et al. (Thu,) studied this question.
synapsesocial.com/papers/6a0f5f1801be78fe815fb1f9 — DOI: https://doi.org/10.1145/3292500.3330723