Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking

Gao Qixiang, Guanting Dong, Yutao Mou, Liwen Wang, Chen Zeng, Daichi Guo, Mingyang Sun, Weiran Xu

Abstract
Collecting dialogue data with domain-slot-value labels for dialogue state tracking (DST) could be a costly process. In this paper, we propose a novel framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST. Specifically, we design an extraction module to extract domain-slot related verbs and nouns in the dialogue. Then, we integrates them into the description, which aims to prompt the model to identify the slot information. Furthermore, we introduce a random sampling strategy to improve the domain generalization ability of the model. We utilize a pre-trained model to encode contexts and description and generates answers with an auto-regressive manner. Experimental results show that our approaches substantially outperform the existing few-shot DST methods on MultiWOZ and gain strong improvements on the slot accuracy comparing to existing slot description methods.
Anthology ID:
2022.emnlp-main.157
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:
2460–2465
Language:
URL:
https://aclanthology.org/2022.emnlp-main.157
DOI:
10.18653/v1/2022.emnlp-main.157
Bibkey:
Cite (ACL):
Gao Qixiang, Guanting Dong, Yutao Mou, Liwen Wang, Chen Zeng, Daichi Guo, Mingyang Sun, and Weiran Xu. 2022. Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2460–2465, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (Qixiang et al., EMNLP 2022)
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