CrossRE: A Cross-Domain Dataset for Relation Extraction

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Relation Extraction (RE) has attracted increasing attention, but current RE evaluation is limited to in-domain evaluation setups. Little is known on how well a RE system fares in challenging, but realistic out-of-distribution evaluation setups. To address this gap, we propose CrossRE, a new, freely-available cross-domain benchmark for RE, which comprises six distinct text domains and includes multi-label annotations. An additional innovation is that we release meta-data collected during annotation, to include explanations and flags of difficult instances. We provide an empirical evaluation with a state-of-the-art model for relation classification. As the meta-data enables us to shed new light on the state-of-the-art model, we provide a comprehensive analysis on the impact of difficult cases and find correlations between model and human annotations. Overall, our empirical investigation highlights the difficulty of cross-domain RE. We release our dataset, to spur more research in this direction.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: EMNLP 2022
Number of pages13
VolumeFindings of the Association for Computational Linguistics: EMNLP 2022
PublisherAssociation for Computational Linguistics
Publication date2022
Pages 3592–3604
Publication statusPublished - 2022


  • Relation Extraction
  • Dataset
  • Cross-domain


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