SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing

Jiaying Hu, Yan Yang, Chencai Chen, Liang He, Zhou Yu


Abstract
Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset.
Anthology ID:
2020.acl-main.567
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6366–6375
Language:
URL:
https://aclanthology.org/2020.acl-main.567
DOI:
10.18653/v1/2020.acl-main.567
Bibkey:
Cite (ACL):
Jiaying Hu, Yan Yang, Chencai Chen, Liang He, and Zhou Yu. 2020. SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6366–6375, Online. Association for Computational Linguistics.
Cite (Informal):
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing (Hu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.567.pdf
Video:
 http://slideslive.com/38929216