Abstract
Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.- Anthology ID:
- P18-2069
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 432–437
- Language:
- URL:
- https://aclanthology.org/P18-2069
- DOI:
- 10.18653/v1/P18-2069
- Cite (ACL):
- Osman Ramadan, Paweł Budzianowski, and Milica Gašić. 2018. Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 432–437, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing (Ramadan et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-2069.pdf
- Code
- additional community code
- Data
- MultiWOZ