8,250 research outputs found
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions for online reinforcement, we devise a novel interactive
learning mechanism based on hamming-diverse beam search for response generation
and one-character user-feedback at each step. Experiments show that our model
inherently promotes the generation of semantically relevant and interesting
responses, and can be used to train agents with customized personas, moods and
conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational
Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active
Learning for Open-Domain Dialogue Generation" on ArXiv
Towards virtual communities on the Web: Actors and audience
We report about ongoing research in a virtual
reality environment where visitors can interact with
agents that help them to obtain information, to perform
certain transactions and to collaborate with them in order
to get some tasks done. Our environment models a
theatre in our hometown. We discuss attempts to let this
environment evolve into a theatre community where we
do not only have goal-directed visitors, but also visitors
that that are not sure whether they want to buy or just
want information or visitors who just want to look
around. It is shown that we need a multi-user and multiagent
environment to realize our goals. Since our environment
models a theatre it is also interesting to investigate
the roles of performers and audience in this environment.
For that reason we discuss capabilities and personalities of agents. Some notes on the historical development of networked communities are included
A Primer for Work-Based Learning: How to Make a Job the Basis for a College Education
Provides an overview of the Jobs to Careers model, in which employers and colleges collaborate to embed curricula and training in the work process, as a way to meet healthcare labor force needs. Includes grantee profiles, lessons learned, and worksheets
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