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.

本文介绍了一种基于最新的神经网络连续学习技术的领域无关的神经对话模型，同时提出了一种新颖的神经连续学习算法，能够以数据 - 效率的方式跨越不同任务积累技能，在客户支持领域通过从合成对话或人类之间的对话到人 - 计算机对话的连续技能转移来验证方法的功效。