Wang, D., Lin, C. orcid.org/0000-0003-3454-2468, Liu, Q. et al. (1 more author) (2021) Fast and scalable dialogue state tracking with explicit modular decomposition. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T. and Zhou, Y., (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 06-11 Jun 2021, Virtual conference. Association for Computational Linguistics (ACL) , pp. 289-295. ISBN 9781954085466
Abstract
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Aug 2021 11:17 |
Last Modified: | 12 Aug 2021 11:17 |
Published Version: | https://aclanthology.org/2021.naacl-main.27/ |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Identification Number: | 10.18653/v1/2021.naacl-main.27 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177028 |