In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning. We describe the architecture of the system as well as the challenges that we faced while building it, like response diversity and scalability. We also introduce a new method for semantic clustering of user-generated content that requires only a modest amount of explicitly labeled data.
翻译:在本文中,我们提出并调查一种新型的自动生成短电子邮件回复端对端方法,称为“智能回馈”。它生成了内容多样的建议,可以作为完整的电子邮件回复,只要在移动上用一个窃听器即可使用。该系统目前用在Gmail的收件箱中,负责协助所有移动响应的10%。该系统的设计是,在非常高的传输量中工作,每天处理数亿条信息。该系统利用了最新的、大规模的深层次学习。我们描述了系统的结构以及我们在建立该系统时面临的挑战,例如反应的多样性和可缩放性。我们还引入了一种对用户生成的内容进行语义组合的新方法,只需要少量的明确标签数据。