@inproceedings{wang-etal-2022-matchprompt,
title = "{M}atch{P}rompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering",
author = "Wang, Jiaxin and
Zhang, Lingling and
Liu, Jun and
Liang, Xi and
Zhong, Yujie and
Wu, Yaqiang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.537",
doi = "10.18653/v1/2022.emnlp-main.537",
pages = "7875--7888",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering
%A Wang, Jiaxin
%A Zhang, Lingling
%A Liu, Jun
%A Liang, Xi
%A Zhong, Yujie
%A Wu, Yaqiang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-matchprompt
%X 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.
%R 10.18653/v1/2022.emnlp-main.537
%U https://aclanthology.org/2022.emnlp-main.537
%U https://doi.org/10.18653/v1/2022.emnlp-main.537
%P 7875-7888
Markdown (Informal)
[MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering](https://aclanthology.org/2022.emnlp-main.537) (Wang et al., EMNLP 2022)
ACL