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.

本文描述了 Smart Compose 系统，它利用大规模神经语言模型和最先进的机器学习技术为 Gmail 用户提供实时的自动建议功能，解决了邮件撰写中的重复输入问题，并采用了特殊的基础设施以实现高吞吐量和实时推理。实验结果表明，所提出的系统具有良好的性能并在 Gmail 中投入使用。