Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
Giovanni A. Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam · 2020
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%.