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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.emnlp-main.537.pdf