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

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