Zero-Shot Dialog Generation with Cross-Domain Latent Actions
Tiancheng Zhao, Maxine Eskénazi · 2018
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.1