SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking

Hwaran Lee, Jinsik Lee, Tae-Yoon Kim


Abstract
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.
Anthology ID:
P19-1546
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5478–5483
Language:
URL:
https://aclanthology.org/P19-1546
DOI:
10.18653/v1/P19-1546
Bibkey:
Cite (ACL):
Hwaran Lee, Jinsik Lee, and Tae-Yoon Kim. 2019. SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5478–5483, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking (Lee et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1546.pdf
Code
 SKTBrain/SUMBT +  additional community code
Data
MultiWOZ