  Smart reply systems have been developed for various messaging platforms. In
this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which
is a key enhanced feature on top of the Uber in-app chat system. It enables
driver-partners to quickly respond to rider messages using smart replies. The
smart replies are dynamically selected according to conversation content using
machine learning algorithms. Our system consists of two major components:
intent detection and reply retrieval, which are very different from standard
smart reply systems where the task is to directly predict a reply. It is
designed specifically for mobile applications with short and non-canonical
messages. Reply retrieval utilizes pairings between intent and reply based on
their popularity in chat messages as derived from historical data. For intent
detection, a set of embedding and classification techniques are experimented
with, and we choose to deploy a solution using unsupervised distributed
embedding and nearest-neighbor classifier. It has the advantage of only
requiring a small amount of labeled training data, simplicity in developing and
deploying to production, and fast inference during serving and hence highly
scalable. At the same time, it performs comparably with deep learning
architectures such as word-level convolutional neural network. Overall, the
system achieves a high accuracy of 76% on intent detection. Currently, the
system is deployed in production for English-speaking countries and 71% of
in-app communications between riders and driver-partners adopted the smart
replies to speedup the communication process.
