Natural language understanding is about applications where there is written or spoken language, and we want the computer to interpret it, then do something with it, possibly generating some language in return. So you can imagine Gmail is a really great place to apply these techniques: the entire application is about the exchange of written language and every day they’re helping people wade through their inboxes. So if Gmail can imbue the product with more language understanding, they can really help their users.

My talk is going to be about Smart Reply. Smart Reply is a feature in Inbox by Gmail that makes it easier and faster to respond on your mobile device. So, if you’re like me, you probably look at emails on your mobile phone, but often postpone responding because typing on a mobile phone is slow and cumbersome and error-prone. What Smart Reply does is it analyzes the contents of the email, and, if the system thinks that a short response is appropriate, it surfaces three suggestions for short responses. Then you, as the person replying, can tap a suggestion (or add to/modify a suggestion) however you want and send it. So the use case here is just helping to make it easier, faster, and less frustrating to respond to messages on your phone device. My goal in talking about Smart Reply is to showcase the power of end-to-end learning from data. In order to have a system that can predict a response in Inbox, the system has to understand how messages and responses work. It’s all about understanding the dialog. Historically, systems that understood dialogue did so in a very rule based way. People would manually program rules and the responses to those rules. Smart Reply is a very different approach. We used a deep learning model called a Recurrent Neural Network. This model learns completely end-to-end from the data itself. Without any feature engineering or manual crafting of rules, we've built a fully data-driven model of conversation. So my goal is to really demonstrate how powerful this technique is to extract complex relationships in the data.