This paper introduces zero-shot dialog generation (ZSDG), as a step towards
neural dialog systems that can instantly generalize to new situations with
minimal data. ZSDG enables an end-to-end generative dialog system to generalize
to a new domain for which only a domain description is provided and no training
dialogs are available. Then a novel learning framework, Action Matching, is
proposed. This algorithm can learn a cross-domain embedding space that models
the semantics of dialog responses which, in turn, lets a neural dialog
generation model generalize to new domains. We evaluate our methods on a new
synthetic dialog dataset, and an existing human-human dialog dataset. Results
show that our method has superior performance in learning dialog models that
rapidly adapt their behavior to new domains and suggests promising future
research.

该研究介绍了零 - shot 对话生成（ZSDG），以实现神经对话系统能够在提供最少数据的情况下即刻推广到新情景。该论文提出了一种新的学习框架 —— 动作匹配算法，能够学习跨领域嵌入空间，使神经对话生成模型能够推广到新的领域。结果显示我们的方法在学习对话模型方面具有优越性能，并具有良好的未来研究前景。