Fast and scalable dialogue state tracking with explicit modular decomposition

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.

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Authors/Creators:	Wang, D. Lin, C. https://orcid.org/0000-0003-3454-2468 Liu, Q. Wong, K.-F.
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:	Published: June 2021
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:	https://doi.org/10.18653/v1/2021.naacl-main.27
Related URLs:	arXiv URL Conference

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Fast and scalable dialogue state tracking with explicit modular decomposition

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