Abstract
We describe two major dialogue system segments: the first is an analysis module that learns to assign dialogue acts from corpora, but on the basis of limited quantities of data, and up to what seems to be some kind of limit on this task, a fact we also discuss. Secondly, we describe a Dialogue Manager which uses a representation of stereotypical dialogue patterns that we call Dialogue Action Frames, which are processed using simple and well understood algorithms, which are adapted from their original role in syntactic analysis role, and which, we believe, generate strong and novel constraints on later access to incomplete dialogue topics.
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Wilks, Y., Webb, N., Setzer, A., Hepple, M., Catizone, R. (2005). Machine Learning Approaches to Human Dialogue Modelling. In: van Kuppevelt, J.C.J., Dybkjær, L., Bernsen, N.O. (eds) Advances in Natural Multimodal Dialogue Systems. Text, Speech and Language Technology, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3933-6_16
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