CrossRE: A Cross-Domain Dataset for Relation Extraction

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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.
TitelFindings of the Association for Computational Linguistics: EMNLP 2022
Antal sider13
Vol/bindFindings of the Association for Computational Linguistics: EMNLP 2022
ForlagAssociation for Computational Linguistics
Sider 3592–3604
StatusUdgivet - 2022


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