Attention Guided Dialogue State Tracking with Sparse Supervision

Understand

Existing approaches to Dialogue State Tracking (DST) rely on turn level dialogue state annotations, which are expensive to acquire in large scale.

• In call centers, for tasks like managing bookings or subscriptions, the user goal can be associated with actions (e.g.~API calls) issued by customer service agents.
• These action logs are available in large volumes and can be utilized for learning dialogue states.
• However, unlike turn-level annotations, such logged actions are only available sparsely across the dialogue, providing only a form of weak supervision for DST models.