Global-Locally Self-Attentive Encoder for Dialogue State Tracking

Victor Zhong, Caiming Xiong, Richard Socher


Abstract
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3% joint goal accuracy and 96.4% request accuracy on the WoZ state tracking task, outperforming prior work by 3.9% and 4.8%. On the DSTC2 task, our model obtains 74.7% joint goal accuracy and 97.3% request accuracy, outperforming prior work by 1.3% and 0.8%
Anthology ID:
P18-1135
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1458–1467
Language:
URL:
https://aclanthology.org/P18-1135
DOI:
10.18653/v1/P18-1135
Bibkey:
Cite (ACL):
Victor Zhong, Caiming Xiong, and Richard Socher. 2018. Global-Locally Self-Attentive Encoder for Dialogue State Tracking. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1458–1467, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Global-Locally Self-Attentive Encoder for Dialogue State Tracking (Zhong et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1135.pdf
Poster:
 P18-1135.Poster.pdf