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. Previous studies adopted a hybrid approach with knowledge-based components to abstract out domain-specific things or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using the recent development 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 the 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 for conversation systems. We verified the efficacy of our method through a conversational skill transfer from 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。

面向对话代理的持续学习