@inproceedings{ramadan-etal-2018-large,
title = "Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing",
author = "Ramadan, Osman and
Budzianowski, Pawe{\l} and
Ga{\v{s}}i{\'c}, Milica",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2069",
doi = "10.18653/v1/P18-2069",
pages = "432--437",
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.",
}
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%0 Conference Proceedings
%T Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
%A Ramadan, Osman
%A Budzianowski, Paweł
%A Gašić, Milica
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F ramadan-etal-2018-large
%X 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.
%R 10.18653/v1/P18-2069
%U https://aclanthology.org/P18-2069
%U https://doi.org/10.18653/v1/P18-2069
%P 432-437
Markdown (Informal)
[Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing](https://aclanthology.org/P18-2069) (Ramadan et al., ACL 2018)
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.