Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training

Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu


Abstract
The challenge of both achieving task completion by querying the knowledge base and generating human-like responses for task-oriented dialogue systems is attracting increasing research attention. In this paper, we propose a “Two-Teacher One-Student” learning framework (TTOS) for task-oriented dialogue, with the goal of retrieving accurate KB entities and generating human-like responses simultaneously. TTOS amalgamates knowledge from two teacher networks that together provide comprehensive guidance to build a high-quality task-oriented dialogue system (student network). Each teacher network is trained via reinforcement learning with a goal-specific reward, which can be viewed as an expert towards the goal and transfers the professional characteristic to the student network. Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student. The usage of discriminators relaxes the rigid coupling between the student and teachers. Extensive experiments on two benchmark datasets (i.e., CamRest and In-Car Assistant) demonstrate that TTOS significantly outperforms baseline methods.
Anthology ID:
2020.emnlp-main.281
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3498–3507
Language:
URL:
https://aclanthology.org/2020.emnlp-main.281
DOI:
10.18653/v1/2020.emnlp-main.281
Bibkey:
Cite (ACL):
Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, and Ruifeng Xu. 2020. Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3498–3507, Online. Association for Computational Linguistics.
Cite (Informal):
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (He et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.281.pdf
Video:
 https://slideslive.com/38938810
Code
 siat-nlp/TTOS