We propose NeuralWOZ, a novel dialogue collection framework that uses
model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector
and Labeler. Collector generates dialogues from (1) user's goal instructions,
which are the user context and task constraints in natural language, and (2)
system's API call results, which is a list of possible query responses for user
requests from the given knowledge base. Labeler annotates the generated
dialogue by formulating the annotation as a multiple-choice problem, in which
the candidate labels are extracted from goal instructions and API call results.
We demonstrate the effectiveness of the proposed method in the zero-shot domain
transfer learning for dialogue state tracking. In the evaluation, the synthetic
dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with
improvements of 4.4% point joint goal accuracy on average across domains, and
improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1
dataset.

本文提出了一种新型的对话收集框架，名为 NeuralWOZ，该框架使用基于模型的对话模拟，并且使用 collector 和 labeler 两个模型，其中 collector 生成对话，而 labeler 则将生成的对话进行注释并将注释形成为一个多项选择问题。这个方法在对话状态跟踪的零样例转移学习中表现出了很好的效果，产生的合成对话语料库在多个领域上以 4.4％的联合目标准确度和 5.7％的待测试覆盖率的改善实现了新的最高水平。