4,699 research outputs found
OCC: A Smart Reply System for Efficient In-App Communications
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.Comment: link to demo: https://www.youtube.com/watch?v=nOffUT7rS0A&t=32
Document-Driven Design for Distributed CAD Services in Service-Oriented Architecture
Current computer-aided design (CAD) systems only support interactive geometry generation, which is not ideal for distributed engineering services in enterprise-to-enterprise collaboration with a generic thin-client service-oriented architecture. This paper proposes a new feature-based modeling mechanism—document-driven design—to enable batch mode geometry construction for distributed CAD systems. A semantic feature model is developed to represent informative and communicative design intent. Feature semantics is explicitly captured as a trinary relation, which provides good extensibility and prevents semantics loss. Data interoperability between domains is enhanced by schema mapping and multiresolution semantics. This mechanism aims to enable asynchronous communication in distributed CAD environments with ease of design alternative evaluation and reuse, reduced human errors, and improved system throughput and utilization
Personalized Maneuver Prediction at Intersections
Losing V, Hammer B, Wersing H. Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama
Personalized Maneuver Prediction at Intersections
Losing V, Hammer B, Wersing H. Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama
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