Skip to main content

Machine Learning Approaches to Human Dialogue Modelling

  • Wilks Y
  • Webb N
  • Setzer A
  • et al.
Citations of this article
Mendeley users who have this article in their library.
Get full text


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.




Wilks, Y., Webb, N., Setzer, A., Hepple, M., & Catizone, R. (2005). Machine Learning Approaches to Human Dialogue Modelling (pp. 355–370).

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free