Statistical User Simulation with a Hidden Agenda
Jost Schatzmann, Blaise Thomson, Steve J. Young · 2007
Recent work in the area of probabilistic user simulation for training statistical dialogue managers has investigated a new agenda-based user model and presented preliminary experiments with a handcrafted model parameter set.Training the model on dialogue data is an important next step, but non-trivial since the user agenda states are not observable in data and the space of possible states and state transitions is intractably large.This paper presents a summary-space mapping which greatly reduces the number of state transitions and introduces a tree-based method for representing the space of possible agenda state sequences.Treating the user agenda as a hidden variable, the forward/backward algorithm can then be successfully applied to iteratively estimate the model parameters on dialogue data.