Zhuoran Wang

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This paper presents a generic dialogue state tracker that maintains beliefs over user goals based on a few simple domainindependent rules, using basic probability operations. The rules apply to observed system actions and partially observable user acts, without using any knowledge obtained from external resources (i.e. without requiring training data). The(More)
This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction(More)
BACKGROUND Electronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually. AIM To develop an algorithm to identify relevant free texts automatically based on labelled examples. METHODS We developed a novel machine learning algorithm, the(More)
We describe a variety of machine-learning techniques that are being applied to social multiuser human--robot interaction using a robot bartender in our scenario. We first present a data-driven approach to social state recognition based on <i>supervised learning</i>. We then describe an approach to social skills execution&#8212;that is, action selection for(More)
This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented, which leads to more natural dialogues. The use of the power set results in a massive(More)
The novel kernel regression model for SMT only demonstrated encouraging results on small-scale toy data sets in previous works due to the complexities of kernel methods. It is the first time results based on the real-world data from the shared translation task will be reported at ACL 2008 Workshop on Statistical Machine Translation. This paper presents the(More)
We describe several forms of machine learning that are being applied to social interaction in Human-Robot Interaction (HRI), using a robot bartender as our scenario. We first present a data-driven approach to social state recognition based on supervised learning. We then describe an approach to social interaction management based on reinforcement learning,(More)
This paper introduces a novel approach to eliminate the domain dependence of dialogue state and action representations, such that dialogue policies trained based on proposed representations can be transferred across different domains. The experimental results show that the policy optimised in a restaurant search domain using our domain-independent(More)