  Goal-oriented dialogue systems are now being widely adopted in industry where
it is of key importance to maintain a rapid prototyping cycle for new products
and domains. Data-driven dialogue system development has to be adapted to meet
this requirement --- therefore, reducing the amount of data and annotations
necessary for training such systems is a central research problem.
  In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet),
a state-of-the-art approach to goal-oriented dialogue generation which only
uses a few example dialogues (i.e. few-shot learning), none of which has to be
annotated. We achieve this by performing a 2-stage training. Firstly, we
perform unsupervised dialogue representation pre-training on a large source of
goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at
the transfer stage, we train DiKTNet using this representation together with 2
other textual knowledge sources with different levels of generality: ELMo
encoder and the main dataset's source domains.
  Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate
our model on it in terms of BLEU and Entity F1 scores, and show that our
approach significantly and consistently improves upon a series of baseline
models as well as over the previous state-of-the-art dialogue generation model,
ZSDG. The improvement upon the latter --- up to 10% in Entity F1 and the
average of 3% in BLEU score --- is achieved using only the equivalent of 10% of
ZSDG's in-domain training data.
