Spoken Dialogue Management Using Probabilistic Reasoning

Spoken Dialogue Management Using Probabilistic Reasoning Nicholas Roy and Joelle Pineau and Sebastian Thrun Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 ����� �  �  �  �� �!�" #$ %'&( *)+�� �,-).�  #/�"012��3��45#6 7# Abstract Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). How- ever, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Pro- cess (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user’s intentions, rather than the system state. We demonstrate that under the same noisy con- ditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue man- ager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy. 1 Introduction The development of automatic speech recognition has made possible more natural human-computer interaction. Speech recognition and speech un- derstanding, however, are not yet at the point where a computer can reliably extract the in- tended meaning from every human utterance. Human speech can be both noisy and ambigu- ous, and many real-world systems must also be speaker-independent. Regardless of these diffi- culties, any system that manages human-machine dialogues must be able to perform reliably even with noisy and stochastic speech input. Recent research in dialogue management has shown that Markov Decision Processes (MDPs) can be useful for generating effective dialogue strategies (Young, 1990; Levin et al., 1998); the system is modelled as a set of states that represent the dialogue as a whole, and a set of actions corre- sponding to speech productions from the system. The goal is to maximise the reward obtained for fulfilling a user’s request. However, the correct way to represent the state of the dialogue is still an open problem (Singh et al., 1999). A common solution is to restrict the system to a single goal. For example, in booking a flight in an automated travel agent system, the system state is described in terms of how close the agent is to being able to book the flight. Such systems suffer from a principal prob- lem. A conventional MDP-based dialogue man- ager must know the current state of the system at all times, and therefore the state has to be wholly contained in the system representation. These systems perform well under certain conditions, but not all. For example, MDPs have been used successfully for such tasks as retrieving e-mail or making travel arrangements (Walker et al., 1998; Levin et al., 1998) over the phone, task domains that are generally low in both noise and ambigu- ity. However, the issue of reliability in the face of noise is a major concern for our application. Our dialogue manager was developed for a mobile robot application that has knowledge from sev- eral domains, and must interact with many peo- ple over time. For speaker-independent systems and systems that must act in a noisy environment, the user’s action …

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