Smart Reply: Automated Response Suggestion for Email

被引:131
作者
Kannan, Anjuli [1 ]
Kurach, Karol [1 ]
Ravi, Sujith [1 ]
Kaufmann, Tobias [1 ]
Tomkins, Andrew [1 ]
Miklos, Balint [1 ]
Corrado, Greg [1 ]
Lukacs, Laszlo [1 ]
Ganea, Marina [1 ]
Young, Peter [1 ]
Ramavajjala, Vivek [1 ]
机构
[1] Google, Mountain View, CA 94043 USA
来源
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2016年
关键词
Email; LSTM; Deep Learning; Clustering; Semantics;
D O I
10.1145/2939672.2939801
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 In box 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.
引用
收藏
页码:955 / 964
页数:10
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