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This paper introduces zero-shot dialog generation (ZSDG), as a step towardsneural dialog systems that can instantly generalize to new situations withminimal data. ZSDG enables an end-to-end generative dialog system to generalizeto a new domain for which only a domain description is provided and no trainingdialogs are available. Then a novel learning framework, Action Matching, isproposed. This algorithm can learn a cross-domain embedding space that modelsthe semantics of dialog responses which, in turn, lets a neural dialoggeneration model generalize to new domains. We evaluate our methods on a newsynthetic dialog dataset, and an existing human-human dialog dataset. Resultsshow that our method has superior performance in learning dialog models thatrapidly adapt their behavior to new domains and suggests promising futureresearch.

Zero-Shot Dialog Generation with Cross-Domain Latent Actions