  Dialogue state tracking is an essential part of goal-oriented dialogue
systems, while most of these state tracking models often fail to handle unseen
services. In this paper, we propose SGD-QA, a simple and extensible model for
schema-guided dialogue state tracking based on a question answering approach.
The proposed multi-pass model shares a single encoder between the domain
information and dialogue utterance. The domain's description represents the
query and the dialogue utterance serves as the context. The model improves
performance on unseen services by at least 1.6x compared to single-pass
baseline models on the SGD dataset. SGD-QA shows competitive performance
compared to state-of-the-art multi-pass models while being significantly more
efficient in terms of memory consumption and training performance. We provide a
thorough discussion on the model with ablation study and error analysis.
