Dialogue state tracking (DST) is at the heart of task-oriented dialogue
systems. However, the scarcity of labeled data is an obstacle to building
accurate and robust state tracking systems that work across a variety of
domains. Existing approaches generally require some dialogue data with state
information and their ability to generalize to unknown domains is limited. In
this paper, we propose using machine reading comprehension (RC) in state
tracking from two perspectives: model architectures and datasets. We divide the
slot types in dialogue state into categorical or extractive to borrow the
advantages from both multiple-choice and span-based reading comprehension
models. Our method achieves near the current state-of-the-art in joint goal
accuracy on MultiWOZ 2.1 given full training data. More importantly, by
leveraging machine reading comprehension datasets, our method outperforms the
existing approaches by many a large margin in few-shot scenarios when the
availability of in-domain data is limited. Lastly, even without any state
tracking data, i.e., zero-shot scenario, our proposed approach achieves greater
than 90% average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.

利用机器阅读理解（RC）方法在对话状态跟踪中，针对模型架构和数据集两个方面进行改进并应用到多种领域，实现了很好的跨领域通用性，并在少数据情形下（few-shot scenarios）表现出了更好的性能，即便在零状态跟踪数据下（zero-shot scenarios），也在 MultiWOZ 2.1 数据集中表现出很高的性能。