Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks

Jie Wu, Ian Harris, Hongzhi Zhao


Abstract
Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge. Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results. However, current joint learning models ignore the following important facts: 1. Long-term slot context is not traced effectively, which is crucial for future slot filling. 2. Slot tagging and intent detection could be mutually rewarding, but bi-directional interaction between slot filling and intent detection remains seldom explored. In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging. Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.
Anthology ID:
2021.naacl-main.63
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
797–806
Language:
URL:
https://aclanthology.org/2021.naacl-main.63
DOI:
10.18653/v1/2021.naacl-main.63
Bibkey:
Cite (ACL):
Jie Wu, Ian Harris, and Hongzhi Zhao. 2021. Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 797–806, Online. Association for Computational Linguistics.
Cite (Informal):
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (Wu et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.63.pdf
Video:
 https://aclanthology.org/2021.naacl-main.63.mp4
Data
ATISSNIPS