Dialogue Management based on Sentence Clustering

Abstract

Dialogue Management (DM) is a key issue in Spoken Dialogue System (SDS). Most of the existing studies on DM use Dialogue Act (DA) to represent semantic information of sentence, which might not represent the nuanced meaning sometimes. In this paper, we model DM based on sentence clusters which have more powerful semantic representation ability than DAs. Firstly, sentences are clustered not only based on the internal information such as words and sentence structures, but also based on the external information such as context in dialogue via Recurrent Neural Networks. Additionally, the DM problem is modeled as a Partially Observable Markov Decision Processes (POMDP) with sentence clusters. Finally, experimental results illustrate that the proposed DM scheme is superior to the existing one.