General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris' distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task's training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED.

通过建立基于Harris的分布式假设的关系模型，以及最近的文本表示学习（特别是BERT），仅从实体相关的文本中构建任务无关的关系表示，我们排除了FewRel的任何训练数据，证明了这些表示在关系提取方面的显著优势。我们还表明，初始化为我们的任务无关表示，然后在监督关系提取数据集上进行调整的模型，在SemEval 2010任务8、KBP37和TACRED上显著优于以前的方法。

匹配空缺：分布相似性用于关系学习