Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam

Abstract:

Zero-shot transfer learning for multi-domain
dialogue state tracking can allow us to handle
new domains without incurring the high cost
of data acquisition. This paper proposes new
zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract dialogue model and the ontology of the domain.
We show that data augmentation through synthesized data can improve the accuracy of
zero-shot learning for both the TRADE model
and the BERT-based SUMBT model on the
MultiWOZ 2.1 dataset. We show training
with only synthesized in-domain data on the
SUMBT model can reach about 2/3 of the accuracy obtained with the full training dataset.
We improve the zero-shot learning state of the
art on average across domains by 21%.