Context Aware Dialog Management with Unsupervised Ranking.

We propose MoveRank, a novel hybrid approach to dialog management that uses a knowledge graph domain structure designed by a domain-expert. The domain encoder converts a symbolic output of the NLU into a vector representation. MoveRank uses an unsupervised similarity measure to obtain the optimal dialog state modifications in a given context. Using a 1K utterance dataset automatically constructed with template expansion from a small set of annotated human-human dialogs, we show that the proposed unsupervised ranking approach produces the correct result on the gold labeled input without spelling variations. Using an encoding method designed to handle spelling variations, MoveRank is correct with $$\mathrm{F}-1=0.86$$ , with the complete set of labels (including intent, entity, and item) and $$\mathrm{F}-1=0.82$$ , with only the intent labels.