论文标题
自我：自我监督的关系特征学习以进行开放关系提取
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
论文作者
论文摘要
开放关系提取是从自然语言句子中提取开放域关系事实的任务。现有的作品要么利用启发式方法或远程监督注释来培训有监督的分类器对预定义的关系，或者采用无监督的方法，而其他假设具有较小的歧视能力。在这项工作中，我们提出了一个名为“自助式”的自我监督框架，该框架通过利用大型审计的语言模型来利用弱，自我监督的信号，以在上下文化的关系特征上自适应聚类，并通过在关系分类中提高上下文化的特征来自适应。三个数据集的实验结果表明，在与竞争基线相比时，自我的有效性和鲁棒性对开放域关系提取的有效性和鲁棒性。
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.
