CrossRE: A Cross-Domain Dataset for Relation Extraction

Elisa Bassignana, Barbara Plank


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.
Anthology ID:
2022.findings-emnlp.263
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3592–3604
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.263
DOI:
10.18653/v1/2022.findings-emnlp.263
Bibkey:
Cite (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.
Cite (Informal):
CrossRE: A Cross-Domain Dataset for Relation Extraction (Bassignana & Plank, Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.263.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.263.mp4