Knowledge discovery in task-oriented dialogue

Abstract

Knowledge discovery is the process of discovering useful knowledge in a broad range of sources, such as relational databases, images, or texts. Dialogues are generated by interaction between people using natural language and can be used as a source of information. Once discovered, knowledge needs to be represented, and there are several approaches to this. In this paper, we propose a method to discover knowledge in task-oriented dialogues by representing these dialogues through folksonomies, using a novel quadripartite model. Folksonomies are knowledge structures composed of users, tags, and resources. Dialogues and folksonomies have a social dimension in common, which renders folksonomies suited to representing knowledge discovered from dialogues. The knowledge represented by folksonomies can be used to interpret new utterances in a dialogue and detect trends, e.g., by discovering Topics Addressed by people at different time intervals, in the dialogues used to learn the folksonomies. The main difference between our approach and past techniques is that we use the characteristics (the content) of each resource in the discovery process. Experiments involving a real-world task-oriented dialogue corpus showed that using our method, learned folksonomies can interpret utterances with an accuracy of 72.32%. Moreover, another experiment showed that it is possible to use our method to determine Topics Addressed by interlocutors in dialogues.