Belief tracking with stacked relational trees

Abstract

We describe a new model for Dialog State Tracking called a Stacked Relational Tree, which naturally models complex relationships between entities across user utterances. It can represent multiple conversational intents and the change of focus between them. Updates to the model are made by a rule-based system in the language of tree regular expressions. We also introduce a probabilistic version that can handle ASR/NLU uncertainty. We show how the parameters can be trained from log data, showing gains on a variety of standard Belief Tracker metrics, and a measurable impact on the success rate of an end-to-end dialog system for TV program discovery.