  Recent studies have shown remarkable success in end-to-end task-oriented
dialog system. However, most neural models rely on large training data, which
are only available for a certain number of task domains, such as navigation and
scheduling.
  This makes it difficult to scalable for a new domain with limited labeled
data. However, there has been relatively little research on how to effectively
use data from all domains to improve the performance of each domain and also
unseen domains. To this end, we investigate methods that can make explicit use
of domain knowledge and introduce a shared-private network to learn shared and
specific knowledge. In addition, we propose a novel Dynamic Fusion Network
(DF-Net) which automatically exploit the relevance between the target domain
and each domain. Results show that our model outperforms existing methods on
multi-domain dialogue, giving the state-of-the-art in the literature. Besides,
with little training data, we show its transferability by outperforming prior
best model by 13.9\% on average.
