SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction

• URL: http://arxiv.org/abs/2004.02438v2
• Date: Tue, 6 Oct 2020 12:32:20 GMT
• Title: SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
• Authors: Xuming Hu, Chenwei Zhang, Yusong Xu, Lijie Wen, Philip S. Yu
• Abstract summary: Open-domain relation extraction is the task of extracting open-domain relation facts from natural language sentences. We proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals. Experimental results on three datasets show the effectiveness and robustness of SelfORE.
• Score: 60.08464995629325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.

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