The best of both worlds: Unifying conventional dialog systems and POMDPs

摘要

Partially observable Markov decision processes (POMDPs) and conventional design practices offer two very different but complementary approaches to building spoken dialog systems. Whereas conventional manual design readily incorporates busi-ness rules, domain knowledge, and contextually appropriate system language, POMDPs employ optimization to produce more detailed dialog plans and better robustness to speech recognition errors. In this paper we propose a novel method for integrating these two approaches, capturing both of their strengths. The POMDP and conventional dialog manager run in parallel; the conventional dialog manager nominates a set of one or more actions, and the POMDP chooses the optimal action. Experiments using a real dialog system confirm that this unified architecture yields better performance than using a conventional dialog manager alone, and also demonstrate an improvement in optimization speed and reliability vs. a pure POMDP.