  Learning with minimal data is one of the key challenges in the development of
practical, production-ready goal-oriented dialogue systems. In a real-world
enterprise setting where dialogue systems are developed rapidly and are
expected to work robustly for an ever-growing variety of domains, products, and
scenarios, efficient learning from a limited number of examples becomes
indispensable.
  In this paper, we introduce a technique to achieve state-of-the-art dialogue
generation performance in a few-shot setup, without using any annotated data.
We do this by leveraging background knowledge from a larger, more highly
represented dialogue source --- namely, the MetaLWOz dataset. We evaluate our
model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human
goal-oriented dialogues in in-car navigation, appointment scheduling, and
weather information domains.
  We show that our few-shot approach achieves state-of-the art results on that
dataset by consistently outperforming the previous best model in terms of BLEU
and Entity F1 scores, while being more data-efficient by not requiring any data
annotation.
