An end-to-end dialog system for TV program discovery

In this paper, we present an end-to-end dialog system for TV program discovery that uniquely combines several technologies such as trainable relation extraction, belief tracking over relational structures, mixed-initiative dialog management, and inference over large-scale knowledge graphs. We present an evaluation of our end-to-end system with real users, and found that our system performed well along several dimensions such as usability and task success rate. These results demonstrate the effectiveness of our system in the target domain.