Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems
This work addresses uncertainty in spoken dialogue systems, which is a domain-specific problem for developers and users, but it appears incremental as it builds on existing POMDP frameworks with new approximations.
The authors tackled the problem of uncertainty in spoken dialogue systems by proposing a novel model based on the partially observable Markov decision process (POMDP) to better handle partially observable states and user intentions, resulting in improved dialogue management through heuristic approximation algorithms and grid point selection methods.
Uncertainty plays a central role in spoken dialogue systems. Some stochastic models like Markov decision process (MDP) are used to model the dialogue manager. But the partially observable system state and user intention hinder the natural representation of the dialogue state. MDP-based system degrades fast when uncertainty about a user's intention increases. We propose a novel dialogue model based on the partially observable Markov decision process (POMDP). We use hidden system states and user intentions as the state set, parser results and low-level information as the observation set, domain actions and dialogue repair actions as the action set. Here the low-level information is extracted from different input modals, including speech, keyboard, mouse, etc., using Bayesian networks. Because of the limitation of the exact algorithms, we focus on heuristic approximation algorithms and their applicability in POMDP for dialogue management. We also propose two methods for grid point selection in grid-based approximation algorithms.