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        <title>CrossRE: A Cross-Domain Dataset for Relation Extraction</title>
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        <name type="personal">
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            <namePart type="given">Yue</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.263</identifier>
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%0 Conference Proceedings
%T CrossRE: A Cross-Domain Dataset for Relation Extraction
%A Bassignana, Elisa
%A Plank, Barbara
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F bassignana-plank-2022-crossre
%X 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.
%R 10.18653/v1/2022.findings-emnlp.263
%U https://aclanthology.org/2022.findings-emnlp.263/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.263
%P 3592-3604

Markdown (Informal)

[CrossRE: A Cross-Domain Dataset for Relation Extraction](https://aclanthology.org/2022.findings-emnlp.263/) (Bassignana & Plank, Findings 2022)

• CrossRE: A Cross-Domain Dataset for Relation Extraction (Bassignana & Plank, Findings 2022)

ACL

• Elisa Bassignana and Barbara Plank. 2022. CrossRE: A Cross-Domain Dataset for Relation Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3592–3604, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.