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