Task-based Virtual Personal Assistants (VPAs) rely on multi-domain
Dialogue State Tracking (DST) models to monitor goals throughout
a conversation. Previously proposed models show promising results
on established benchmarks, but they have difficulty adapting to
unseen domains due to domain-specific parameters in their model
architectures. We propose a new Similarity-based Multi-domain Dialogue State Tracking model (SM-DST) that uses retrieval-inspired
and fine-grained contextual token-level similarity approaches to
efficiently and effectively track dialogue state. The key difference
with state-of-the-art DST models is that SM-DST has a single model
with shared parameters across domains and slots. Because we base
SM-DST on similarity it allows the transfer of tracking information between semantically related domains as well as to unseen
domains without retraining. Furthermore, we leverage copy mechanisms that consider the system’s response and the dialogue state
from previous turn predictions, allowing it to more effectively track
dialogue state for complex conversations. We evaluate SM-DST
on three variants of the MultiWOZ DST benchmark datasets. The
results demonstrate that SM-DST significantly and consistently
outperforms state-of-the-art models across all datasets by absolute
5-18% and 3-25% in the few- and zero-shot settings, respectively.