Integrating Pre-trained Model into Rule-based Dialogue Management
This work addresses the problem of scalability and maintenance in industrial task-oriented dialogue systems, offering an incremental improvement by combining rule-based and data-driven approaches.
The paper tackles the challenge of maintaining rule-based dialogue systems as scenarios become complex by integrating pre-trained models to make the dialogue manager trainable and scalable, achieving strong few-shot capability in experiments.
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 interpretable. 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.