SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Xuming Hu | Lijie Wen | Yusong Xu | Chenwei Zhang | Philip Yu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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 propose 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.