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In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Abstract This paper proposes a novel integrated dialog simulation technique for(More)
This paper proposes a probabilistic framework for spoken dialog management using dialog examples. To overcome the complexity problems of the classic partially observable Mar-kov decision processes (POMDPs) based dialog manager, we use a frame-based belief state representation that reduces the complexity of belief update. We also used dialog examples to(More)
This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active(More)
This paper proposes a novel user intention simulation method which is a data-driven approach but able to integrate diverse user discourse knowledge together to simulate various type of users. In Markov logic framework, logistic regression based data-driven user intention modeling is introduced, and human dialog knowledge are designed into two layers such as(More)
This paper proposes a method to model confirmations for example-based dialog management. To enable the system to provide a confirmation to the user in an appropriate time, we employed a multiple dialog state representation approach for keeping track of user input uncertainty and implemented a confirmation agent which decides when the information gathered(More)
—Query relaxation refers to the process of reducing the number of constraints on a query if it returns no result when searching a database. This is an important process to enable extraction of an appropriate number of query results because queries that are too strictly constrained may return no result, whereas queries that are too loosely constrained may(More)
This paper proposes an unsupervised spoken language understanding (SLU) framework for a multi-domain dialog system. Our unsupervised SLU framework applies a non-parametric Bayesian approach to dialog acts, intents and slot entities, which are the components of a semantic frame. The proposed approach reduces the human effort necessary to obtain a(More)
In data-driven spoken dialog system development, developers should prepare a dialog corpus with semantic annotation. However, the labeling process is a laborious and time consuming task. To reduce human efforts, we propose an unsupervised approach based on non-parametric Bayesian Hidden Markov Model to the problem of modeling user actions. With the(More)