An Exploration of Prompt-Based Zero-Shot Relation Extraction Method

Zhao Jun, Hu Yuan, Xu Nuo, Gui Tao, Zhang Qi, Chen Yunwen, Gao Xiang


Abstract
“Zero-shot relation extraction is an important method for dealing with the newly emerging relations in the real world which lacks labeled data. However, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the labeled data of predefined relations are extremely scarce. To maximize the efficiency of data exploitation, instead of directly fine-tuning, we introduce a prompt-tuning technique to elicit the existing relational knowledge in pre-trained language model (PLMs). In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods. To break through the bottleneck, we model the semantic interaction between relational instances and their descriptions directly during encoding. Experiment results on two academic datasets show that (1) our method outperforms the previous state-of-the-art method by a large margin with different samples of predefined relations; (2) this advantage will be further amplified in the low-resource scenario.”
Anthology ID:
2022.ccl-1.70
Volume:
Proceedings of the 21st Chinese National Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Nanchang, China
Editors:
Maosong Sun (孙茂松), Yang Liu (刘洋), Wanxiang Che (车万翔), Yang Feng (冯洋), Xipeng Qiu (邱锡鹏), Gaoqi Rao (饶高琦), Yubo Chen (陈玉博)
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
786–797
Language:
English
URL:
https://aclanthology.org/2022.ccl-1.70
DOI:
Bibkey:
Cite (ACL):
Zhao Jun, Hu Yuan, Xu Nuo, Gui Tao, Zhang Qi, Chen Yunwen, and Gao Xiang. 2022. An Exploration of Prompt-Based Zero-Shot Relation Extraction Method. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 786–797, Nanchang, China. Chinese Information Processing Society of China.
Cite (Informal):
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method (Jun et al., CCL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ccl-1.70.pdf
Data
FewRelTACRED