Integrating Pre-trained Model into Rule-based Dialogue Management [article]

Jun Quan, Meng Yang, Qiang Gan, Deyi Xiong, Yiming Liu, Yuchen Dong, Fangxin Ouyang, Jun Tian, Ruiling Deng, Yongzhi Li, Yang Yang, Daxin Jiang
2021 arXiv   pre-print
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to maintain the dialogue logic when the scenarios get more and more complex. On the other hand, data-driven dialogue systems, usually with end-to-end structures, are popular in academic research and easier to deal with complex conversations, but such methods require plenty of training data and the behaviors are less
more » ... retable. In this paper, we propose a method to leverages the strength of both rule-based and data-driven dialogue managers (DM). We firstly introduce the DM of Carina Dialog System (CDS, an advanced industrial dialogue system built by Microsoft). Then we propose the "model-trigger" design to make the DM trainable thus scalable to scenario changes. Furthermore, we integrate pre-trained models and empower the DM with few-shot capability. The experimental results demonstrate the effectiveness and strong few-shot capability of our method.
arXiv:2102.08553v1 fatcat:lvkvovokhvdczakhgqyxpj2nzy