  Dialogue State Tracking (DST), a crucial component of task-oriented dialogue
(ToD) systems, keeps track of all important information pertaining to dialogue
history: filling slots with the most probable values throughout the
conversation. Existing methods generally rely on a predefined set of values and
struggle to generalise to previously unseen slots in new domains. To overcome
these challenges, we propose a domain-agnostic extractive question answering
(QA) approach with shared weights across domains. To disentangle the complex
domain information in ToDs, we train our DST with a novel domain filtering
strategy by excluding out-of-domain question samples. With an independent
classifier that predicts the presence of multiple domains given the context,
our model tackles DST by extracting spans in active domains. Empirical results
demonstrate that our model can efficiently leverage domain-agnostic QA datasets
by two-stage fine-tuning while being both domain-scalable and open-vocabulary
in DST. It shows strong transferability by achieving zero-shot
domain-adaptation results on MultiWOZ 2.1 with an average JGA of 36.7%. It
further achieves cross-lingual transfer with state-of-the-art zero-shot
results, 66.2% JGA from English to German and 75.7% JGA from English to Italian
on WOZ 2.0.
