Blog

The latest news from Google AI

Smart Compose: Using Neural Networks to Help Write Emails

Wednesday, May 16, 2018

Posted by Yonghui Wu, Principal Engineer, Google Brain TeamGoogle I/OSmart ComposeSmart Reply

Latency: Since Smart Compose provides predictions on a per-keystroke basis, it must respond ideally within 100ms for the user not to notice any delays. Balancing model complexity and inference speed was a critical issue.

Scale: Gmail is used by more than 1.4 billion diverse users. In order to provide auto completions that are useful for all Gmail users, the model has to have enough modeling capacity so that it is able to make tailored suggestions in subtly different contexts.

Fairness and Privacy: In developing Smart Compose, we needed to address sources of potential bias in the training process, and had to adhere to the same rigorous user privacy standards as Smart Reply, making sure that our models never expose user’s private information. Furthermore, researchers had no access to emails, which meant they had to develop and train a machine learning system to work on a dataset that they themselves cannot read.

Finding the Right Model ngramneural bag-of-wordsRNN languagesequence-to-sequenceword embeddings

Smart Compose RNN-LM model architecture. Subject and previous email message are encoded by averaging the word embeddings in each field. The averaged embeddings are then fed to the RNN-LM at each decoding step.

Accelerated Model Training & ServingTPUv2 PodFairness and PrivacyFairness in machine learningSemantics derived automatically from language corpora contain human-like biasesthis paperFuture workTransformer,RNMT+AcknowledgementsSmart Compose language generation model was developed by Benjamin Lee, Mia Chen, Gagan Bansal, Justin Lu, Jackie Tsay, Kaushik Roy, Tobias Bosch, Yinan Wang, Matthew Dierker, Katherine Evans, Thomas Jablin, Dehao Chen, Vinu Rajashekhar, Akshay Agrawal, Yuan Cao, Shuyuan Zhang, Xiaobing Liu, Noam Shazeer, Andrew Dai, Zhifeng Chen, Rami Al-Rfou, DK Choe, Yunhsuan Sung, Brian Strope, Timothy Sohn, Yonghui Wu, and many others.