Global-Locally Self-Attentive Encoder for Dialogue State Tracking
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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-1135.pdf