By transferring latent dialogue knowledge from multiple sources of varying generality, we obtained a model with superior generalization to an underrepresented domain.
we showed that our few-shot approach achieves state-of-the art results on the Stanford Multi-Domain dataset while being more dataefficient than the previous best model, by requiring significantly less data none of which has to be annotated.