Abstract
The previous chapters of this thesis have described statistical methods for policy learning and for handling uncertainty in a spoken dialogue system. Although the parameters are a key component of the statistical models underlying these methods, all experiments thus far have assumed them to be given. Chapter 5 has discussed the optimisation of the policy parameters which describe the policy. This chapter will discuss the optimisation of the parameters of the Bayesian network used for user modeling. These include the probabilities for changes in user goals and the probability of user actions given the goals.
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Notes
- 1.
Note that the \(H_{c,u}\) sets are dependendent on the machine act, the concept, and user act.
- 2.
The Cartesian product, \(H_u=\prod\nolimits _c H_{c,u}\) is the set \(\left\{ \mathbf{g }: \forall k, g_k \in H_{u,k} \right\} \).
- 3.
The given equations do not take into account the possibility of division by zero. This is ignored here, but can easily be included without any extra computational burden.
- 4.
Note that the additional 216 dialogues were recorded using a separate Hidden Information State dialogue manager not described here.
- 5.
The separate trial used different subjects and different task specifications.
References
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Thomson, B. (2013). Parameter Learning. In: Statistical Methods for Spoken Dialogue Management. Springer Theses. Springer, London. https://doi.org/10.1007/978-1-4471-4923-1_7
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DOI: https://doi.org/10.1007/978-1-4471-4923-1_7
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