  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.
