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Dialogue State Tracking (DST) is a core component of virtual assistants suchas Alexa or Siri. To accomplish various tasks, these assistants need to supportan increasing number of services and APIs. The Schema-Guided State Trackingtrack of the 8th Dialogue System Technology Challenge highlighted the DSTproblem for unseen services. The organizers introduced the Schema-GuidedDialogue (SGD) dataset with multi-domain conversations and released a zero-shotdialogue state tracking model. In this work, we propose a GOaL-OrientedMulti-task BERT-based dialogue state tracker (GOLOMB) inspired by architecturesfor reading comprehension question answering systems. The model "queries"dialogue history with descriptions of slots and services as well as possiblevalues of slots. This allows to transfer slot values in multi-domain dialoguesand have a capability to scale to unseen slot types. Our model achieves a jointgoal accuracy of 53.97% on the SGD dataset, outperforming the baseline model.

Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker