RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset

Understand

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.