A Teacher-Student Framework for Maintainable Dialog Manager

By Weikang Wang, Jiajun Zhang, Han Zhang, Mei-Yuh Hwang, Chengqing Zong, Zhifei Li. 2018

7 citations recorded by the source corpus.

Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the “student” is an extended dialog manager based on a new ontology, and the “teacher” is existing resources used for guiding the learning process of the “student”. By specifying constraints held in the new dialog manager, we transfer knowledge of the “teacher” to the “student” without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jianping Gou
2. Dacheng Tao
3. Baosheng Yu
4. S. Maybank
5. Chengqing Zong
6. Jiajun Zhang
7. Weikang Wang
8. Q. Li

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “…extended to other visual recognition applications, including face recognition (Luo et al. 2016; Kong et al. 2019; Yan et al. 2019; Ge et al. 2018; Wang et al. 2018b, 2019c; Duong et al. 2019; Wu et al. 2020; Wang et al. 2017; Zhang et al. 2020b; Wang et al. 2020b), image/video segmentation (He…”
• Incremental Learning from Scratch for Task-Oriented Dialogue Systems (2019, background): “Such systems work well if all possible combinations of user inputs and conditions are considered in the training stage (Paek and Pierac-cini, 2008; Wang et al., 2018).”