知识对话系统的零资源在线更新

Zero Resource Online Update for Knowledge-Grounded Dialogue System

知识对话系统旨在使用外部知识和对话上下文生成符合客观事实的回复。已有知识对话的研究较少关注知识对话系统的在线更新的问题。在知识对话系统在线更新中,面临因与知识配对的对话语料标注成本过高而导致零对话语料可用的问题。该文针对知识对话系统零资源更新问题,提出使用Pseudo Data进行模型的在线更新。首先,针对不同的场景,分析成因并提出了不同的Pseudo Data生成策略。此外,该文在数据集KdConv上验证了当对话语料零资源时该文提出的方法的有效性。实验结果表明,使用PseudoData进行更新的模型在知识利用率、主题相关性上接近使用人类标注数据的在线更新模型,能有效使得知识对话系统在对话语料零资源的情况下完成在线更新。

The knowledge-grounded dialogue systems are designed to use external knowledge and conversation contexts to generate responses that conform to objective facts. Its online update, which is seldom addressed, is challenged by the zero resource setting due to the high cost of labeling the dialogue corpus. This paper proposes a method to update the model parameter with zero resource setting via pseudo data. First of all, we design different pseudo data generation strategies for different scenarios. Verified on the KdConv dataset, the experimental results show that the proposed method is comparable to human annotated data in terms of knowledge utilization and topic relevance.