Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.
翻译:Openlation Explicationon (OpenRE) 旨在从开放域中发现新关系。 以前的 OpenRE 方法主要有两个问题:(1) 缺乏区分已知关系和新关系的能力。 当将常规测试设置扩展至更一般的设置,使测试数据也可能来自可见类别时,现有方法的性能显著下降。 (2) 在实际应用之前,必须进行二级标签。 现有方法不能为下游任务所急需的新关系标出人类可读和有意义的类型。 为了解决这些问题,我们提议了主动发现发现(ARC) 框架, 该框架利用对已知关系和新关系的歧视, 并涉及对新关系进行积极学习。 对三种真实世界数据集的广泛实验显示, ARD 大大超越了常规和我们提议的通用 OpenRE 设置的以往最新方法。 源码和数据集将可供复制。