Statistical Dialogue Management using Intention Dependency Graph
Koichiro Yoshino, Shinji Watanabe, Jonathan Le Roux, John R. Hershey · 2013
We present a method of statistical dia-logue management using a directed inten-tion dependency graph (IDG) in a par-tially observable Markov decision pro-cess (POMDP) framework. The transition probabilities in this model involve infor-mation derived from a hierarchical graph of intentions. In this way, we combine the deterministic graph structure of a con-ventional rule-based system with a statis-tical dialogue framework. The IDG also provides a reasonable constraint on a user simulation model, which is used when learning a policy function in POMDP and dialogue evaluation. Thus, this method converts a conventional dialogue manager to a statistical dialogue manager that uti-lizes task domain knowledge without an-notated dialogue data. 1