MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

Jiaxin Wang, Lingling Zhang, Jun Liu, Xi Liang, Yujie Zhong, Yaqiang Wu


Abstract
Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few pre-defined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE.
Anthology ID:
2022.emnlp-main.537
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:
7875–7888
Language:
URL:
https://aclanthology.org/2022.emnlp-main.537
DOI:
10.18653/v1/2022.emnlp-main.537
Bibkey:
Cite (ACL):
Jiaxin Wang, Lingling Zhang, Jun Liu, Xi Liang, Yujie Zhong, and Yaqiang Wu. 2022. MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7875–7888, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (Wang et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.537.pdf