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This paper describes and compares two methods for simulating user behaviour in spoken dialogue systems. User simulations are important for automatic dialogue strategy learning and the evaluation of competing strategies. Our methods are designed for use with " Information State Update " (ISU)-based dialogue systems. The first method is based on supervised(More)
We propose the " advanced " n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and " normal " n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature(More)
We demonstrate a multimodal dialogue system using reinforcement learning for in-car scenarios , developed at Edinburgh University and Cambridge University for the TALK project 1. This prototype is the first " Information State Update " (ISU) dialogue system to exhibit reinforcement learning of dialogue strategies, and also has a fragmentary clarification(More)
The lack of suitable training and testing data is currently a major roadblock in applying machine-learning techniques to dialogue management. Stochastic modelling of real users has been suggested as a solution to this problem, but to date few of the proposed models have been quantitatively evaluated on real data. Indeed , there are no established criteria(More)
We present and evaluate an automatic annotation system which builds " Information State Update " (ISU) representations of dialogue context for the COMMUNICATOR (2000 and 2001) corpora of human-machine dialogues (approx 2300 dialogues). The purposes of this annotation are to generate training data for reinforcement learning (RL) of dialogue policies, to(More)
We propose a method for learning dialogue management policies from a fixed data set. The method addresses the challenges posed by Information State Update (ISU)-based dialogue systems, which represent the state of a dialogue as a large set of features, resulting in a very large state space and a huge policy space. To address the problem that any fixed data(More)
We report evaluation results for real users of a learnt dialogue management policy versus a hand-coded policy in the TALK project's " TownInfo " tourist information system [1]. The learnt policy, for filling and confirming information slots, was derived from COMMUNICATOR (flight-booking) data using Reinforcement Learning (RL) as described in [2], ported to(More)
In this paper we build user simulations of older and younger adults using a corpus of interactions with a Wizard-of-Oz appointment scheduling system. We measure the quality of these models with standard metrics proposed in the literature. Our results agree with predictions based on statistical analysis of the corpus and previous findings about the diversity(More)
We present SimSensei Kiosk, an implemented virtual human interviewer designed to create an engaging face-to-face interaction where the user feels comfortable talking and sharing information. SimSensei Kiosk is also designed to create in-teractional situations favorable to the automatic assessment of distress indicators, defined as verbal and nonverbal(More)