Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations. To draw the connection between entity and relation, we design a proto-level fusion module to boost the performance of both entity extraction and relation classification. Moreover, a new cross-domain few-shot triple extraction task is introduced. Extensive experiments show that our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and 20.5 F1 score on FewRel 2.0 (cross-domain).

翻译：知识图谱（KGs）包含大量实体-关系-实体三元组，为下游应用提供了丰富的信息。尽管从非结构化文本中抽取三元组已被广泛探索，但大多数方法需要大量标注样本。当仅有少量标注数据可用时，性能会急剧下降。为解决这一问题，我们提出了一种面向关系三元组抽取的相互引导少样本学习框架（MG-FTE）。具体而言，我们的方法包含一个实体引导的关系原型解码器，用于首先对关系进行分类；以及一个关系引导的实体原型解码器，用于基于已分类的关系抽取实体。为了建立实体与关系之间的关联，我们设计了一个原型级融合模块，以提升实体抽取和关系分类的性能。此外，我们还引入了一项新的跨领域少样本三元组抽取任务。大量实验表明，我们的方法在FewRel 1.0（单领域）上的F1值比许多最先进方法高出12.6，在FewRel 2.0（跨领域）上高出20.5。