Manotumruksa, J., Dalton, J. , Meij, E. and Yilmaz, E. (2021) Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation. In: Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic, 07-11 Nov 2021, pp. 1674-1683. ISBN 9781955917100
Text
256991.pdf - Published Version Available under License Creative Commons Attribution. 615kB |
Publisher's URL: https://aclanthology.org/2021.findings-emnlp.144
Abstract
While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy. Although several approaches recently propose augmenting the training set by copying user utterances and replacing the real slot values with other possible or even similar values, they are not effective at improving the performance of existing DST models. To address these challenges, we propose a Turn-based Loss Function (TLF) that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns in order to improve joint goal accuracy. We also propose a simple but effective Sequential Data Augmentation (SDA) algorithm to generate more complex user utterances and system responses to effectively train existing DST models. Experimental results on two standard DST benchmark collections demonstrate that our proposed TLF and SDA techniques significantly improve the effectiveness of the state-of-the-art DST model by approximately 7-8% relative reduction in error and achieves a new state-of-the-art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOZ2.2, respectively.
Item Type: | Conference Proceedings |
---|---|
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Dalton, Dr Jeff |
Authors: | Manotumruksa, J., Dalton, J., Meij, E., and Yilmaz, E. |
College/School: | College of Science and Engineering > School of Computing Science |
ISBN: | 9781955917100 |
Published Online: | 01 November 2021 |
Copyright Holders: | Copyright © 2021 The Association for Computational Linguistics |
Publisher Policy: | Reproduced under a Creative Commons licence |
Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record