Knowledge discovery in task-oriented dialogue
Résumé
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.knowl