Research Article
A Tree-Based Approach for Building Efficient Task-Oriented Dialogue Systems
@INPROCEEDINGS{10.1007/978-3-030-90196-7_45, author={Tao Gan and Chunang Li and Yuhui Xi and Yanmin He}, title={A Tree-Based Approach for Building Efficient Task-Oriented Dialogue Systems}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Task-oriented Rule-based Data-driven Intent-slot tree}, doi={10.1007/978-3-030-90196-7_45} }- Tao Gan
Chunang Li
Yuhui Xi
Yanmin He
Year: 2021
A Tree-Based Approach for Building Efficient Task-Oriented Dialogue Systems
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_45
Abstract
Task-oriented dialogue systems have attracted increasing attention. The traditional rule-based approaches suffer from limited generalization ability as well as the high cost of system deployment, whereas the data-driven deep learning approaches are data-hungry and the domain-specific data is insufficient for full training their models. In this paper, we present a hybrid method which combines the strengths of both rule-based and data-driven approaches. We first establish intent-slot trees from the standard multi-turn dialogue corpus in specific domain. During the dialogue, the power of deep language understanding model is exploited to enhance the generalization ability of the system and the multi-turn dialogue proceeds following the path of the intent-slot tree established. Experimental results show that the proposed approach achieves superior performance over deep learning ones which demonstrates its effectiveness in building task-oriented dialogue systems under a limited amount of training data.
