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
- 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)
- PDF:
- https://preview.aclanthology.org/gem-23-ingestion/2022.findings-emnlp.263.pdf