MetaASSIST: Robust Dialogue State Tracking with Meta Learning

Fanghua Ye, Xi Wang, Jie Huang, Shenghui Li, Samuel Stern, Emine Yilmaz


Abstract
Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to generate pseudo labels for the noisy training set. These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model. Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging. Moreover, a single parameter shared by all slots and all instances may be suboptimal. To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter. Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions. These functions are trained in a meta-learning manner by taking the validation set as meta data. Experimental results demonstrate that all three schemes can achieve competitive performance. Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4.
Anthology ID:
2022.emnlp-main.76
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1157–1169
Language:
URL:
https://aclanthology.org/2022.emnlp-main.76
DOI:
10.18653/v1/2022.emnlp-main.76
Bibkey:
Cite (ACL):
Fanghua Ye, Xi Wang, Jie Huang, Shenghui Li, Samuel Stern, and Emine Yilmaz. 2022. MetaASSIST: Robust Dialogue State Tracking with Meta Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1157–1169, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
MetaASSIST: Robust Dialogue State Tracking with Meta Learning (Ye et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.76.pdf