Spoken Dialogue Management Using Probabilistic Reasoning
نویسندگان
چکیده
Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process (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 conditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue manager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy.
منابع مشابه
On-Line Learning of a Persian Spoken Dialogue System Using Real Training Data
The first spoken dialogue system developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (g) on the learning speed...
متن کاملOn-Line Learning of a Persian Spoken Dialogue System Using Real Training Data
The first spoken dialogue system developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (g) on the learning speed...
متن کاملSpoken Dialogue Management Using Probabilistic Reasoning
Spoken dialogue managers have benefited from stochastic planners such as MDPs. However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a 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 compare the performance of MDP and POMDP dialogue manage...
متن کاملDialogue Management in the EMBASSI Realm Using Description Logics to Reason about Ontology Concepts
The integration of utterances in their contexts, by the help of reasoning about the concepts of the domain – organized in a formal ontology and laid down in Description Logics –, enables successful dialogue management for spoken language. We outline our dialogue model and our approach of establishing a formal ontology within the
متن کاملOptimizing Dialogue Strategy Learning Using Learning Automata
Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlyi...
متن کامل