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This paper presents work on using Bayesian networks for the dialogue act recognition module of a dialogue system for Dutch dialogues. The Bayesian networks can be constructed from the data in an annotated dialogue corpus. For two series of experiments -using different corpora but the same annotation scheme -recognition results are presented and evaluated.

While data-driven methods for spoken language understanding reduce maintenance and portability costs compared with handcrafted parsers, the collection of word-level semantic annotations for training remains a time-consuming task. A recent line of research has focused on building generative models from unaligned semantic representations, using expectation-maximisation techniques to align semantic concepts. This paper presents an efficient, simple technique that parses a semantic tree by recursively calling discriminative semantic classification models. Results show that it outperforms existing generative models, while performance is close to more complex grammar induction techniques. We also show that our method is robust to speech recognition errors, by improving over a handcrafted parser previously used for dialogue data collection.

We describe a variety of machine learning techniques that are being applied to social multi-user human-robot interaction, using a robot bartender in our scenario. We first present a data-driven approach to social state recognition based on supervised learning. We then describe an approach to social skills execution-i.e., action selection for generating socially appropriate robot behaviour-which is based on reinforcement learning, using a data-driven simulation of multiple users to train execution policies for social skills. Next, we describe how these components for social state recognition and skills execution have been integrated into an end-to-end robot bartender system, and we discuss the results of a user evaluation. Finally, we present an alternative unsupervised learning framework that combines social state recognition and social skills execution, based on hierarchical Dirichlet processes and an infinite POMDP interaction manager. The models make use of data from both human-human interactions collected in a number of German bars and human-robot interactions recorded in the evaluation of an initial version of the system.

This paper investigates the claim that a dialogue manager modelled as a Partially Observable Markov Decision Process (POMDP) can achieve improved robustness to noise compared to conventional state-based dialogue managers. Using the Hidden Information State (HIS) POMDP dialogue manager as an exemplar, and an MDP-based dialogue manager as a baseline, evaluation results are presented for both simulated and real dialogues in a Tourist Information Domain. The results on the simulated data show that the inherent ability to model uncertainty, allows the POMDP model to exploit alternative hypotheses from the speech understanding system. The results obtained from a user trial show that the HIS system with a trained policy performed significantly better than the MDP baseline.