  While end-to-end neural conversation models have led to promising advances in
reducing hand-crafted features and errors induced by the traditional complex
system architecture, they typically require an enormous amount of data due to
the lack of modularity. Previous studies adopted a hybrid approach with
knowledge-based components either to abstract out domain-specific information
or to augment data to cover more diverse patterns. On the contrary, we propose
to directly address the problem using recent developments in the space of
continual learning for neural models. Specifically, we adopt a
domain-independent neural conversational model and introduce a novel neural
continual learning algorithm that allows a conversational agent to accumulate
skills across different tasks in a data-efficient way. To the best of our
knowledge, this is the first work that applies continual learning to
conversation systems. We verified the efficacy of our method through a
conversational skill transfer from either synthetic dialogs or human-human
dialogs to human-computer conversations in a customer support domain.
