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%.

本文提出了一种采用零样本迁移学习的技术，通过从域本体和抽象对话模型中合成所有域内训练数据，对多域对话状态跟踪进行了改进，表明对话状态跟踪中的数据增强可以提高TRADE模型和基于BERT的SUMBT模型在MultiWOZ2.1数据集上零样本学习的准确性，并表明只用合成的域内数据对SUMBT模型进行训练，可以达到全局数据集训练的2/3的准确度，文章的方法还提高了领域之间的平均零样本学习水平达到21%

使用合成数据进行零样本迁移学习的多领域对话状态跟踪