# Integrating Pre-trained Model into Rule-based Dialogue Management

arXiv: https://arxiv.org/abs/2102.08553 · HTML: https://mlsanity.com/paper/2102.08553/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Conditional Generation and Snapshot Learning in Neural Dialogue Systems](https://mlsanity.com/paper/1606.03352/) (2016)

## Built on by (most cited first, 1 of 1)

- [MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning](https://mlsanity.com/paper/2306.02252/) (2023)

## Abstract (arXiv)

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.