  Domain Adaptation arises when we aim at learning from source domain a model
that can per- form acceptably well on a different target domain. It is
especially crucial for Natural Language Generation (NLG) in Spoken Dialogue
Systems when there are sufficient annotated data in the source domain, but
there is a limited labeled data in the target domain. How to effectively
utilize as much of existing abilities from source domains is a crucial issue in
domain adaptation. In this paper, we propose an adversarial training procedure
to train a Variational encoder-decoder based language generator via multiple
adaptation steps. In this procedure, a model is first trained on a source
domain data and then fine-tuned on a small set of target domain utterances
under the guidance of two proposed critics. Experimental results show that the
proposed method can effec- tively leverage the existing knowledge in the source
domain to adapt to another related domain by using only a small amount of
in-domain data.
