MA-DST: Multi-Attention Based Scalable Dialog State Tracking

Understand

Task oriented dialog agents provide a natural language interface for users to complete their goal.

• Dialog State Tracking (DST), which is often a core component of these systems, tracks the system's understanding of the user's goal throughout the conversation.
• To enable accurate multi-domain DST, the model needs to encode dependencies between past utterances and slot semantics and understand the dialog context, including long-range cross-domain references.
• We introduce a novel architecture for this task to encode the conversation history and slot semantics more robustly by using attention mechanisms at multiple granularities.