RED$^{\ m FM}$：一个过滤且多语言的关系抽取数据集
RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset
AI总结：
本文提出RED$^{\ m FM}$数据集及SRED$^{\ m FM}$，用于多语言关系抽取，并首次实验端到端多语言模型mREBEL，以解决现有数据集覆盖不足的问题。
摘要：
关系抽取（RE）是一项识别文本中实体之间关系的任务，它能够获取关系事实，并弥合自然语言与结构化知识之间的鸿沟。然而，当前的关系抽取模型往往依赖于覆盖关系类型较少的小型数据集，尤其是在处理英语以外的语言时。在本文中，我们解决了上述问题，并提供了两种新资源，用于训练和评估多语言关系抽取系统。首先，我们提出了SRED$^{\ m FM}$，一个自动标注的数据集，覆盖18种语言、400种关系类型、13种实体类型，总计超过4000万个三元组实例。其次，我们提出了RED$^{\ m FM}$，一个较小、经过人工修订的数据集，涵盖七种语言，用于评估多语言关系抽取系统。为了展示这些新数据集的实用性，我们实验了第一个端到端的多语言关系抽取模型mREBEL，该模型可提取多种语言的三元组，包括实体类型。我们在https://www.github.com/babelscape/rebel发布了我们的资源和模型检查点。
英文摘要：
Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. First, we present SRED$^{\rm FM}$, an automatically annotated dataset covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances. Second, we propose RED$^{\rm FM}$, a smaller, human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems. To demonstrate the utility of these novel datasets, we experiment with the first end-to-end multilingual RE model, mREBEL, that extracts triplets, including entity types, in multiple languages. We release our resources and model checkpoints at https://www.github.com/babelscape/rebel
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
