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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/P19-1546.pdf
- Code
- SKTBrain/SUMBT + additional community code
- Data
- MultiWOZ