A dialogue management system using a corpus-based framework and a dynamic dialogue transition model

Abstract

We propose a novel practical dialogue management system that satisfies the requirements of robust dialogue management, efficient domain knowledge construction, and flexible architecture for maintenance and extensibility. The proposed system uses a corpus-based framework and a dynamic dialogue transition network model, which work in a cooperative and complementary manner. The former supports automatic generation of domain knowledge from an annotated corpus, whereas the latter manages dialogue flows robustly. The system can also automatically carry out user-intention analyses and response generation since it retrieves the most similar utterance and its response pair by estimating similarity between the input utterance and corpus utterances. Therefore, the system can control a new domain dialogue by updating the corpus. In our experiments on two different corpora, the system achieved F0.5-scores of 91% and 90% in terms of user intention recognition with a task completion rate of 95% and 91%.