Abstract
Our goal is to study the novel task of distant supervision for multilingual relation extraction (Multi DS-RE). Research in Multi DS-RE has remained limited due to the absence of a reliable benchmarking dataset. The only available dataset for this task, RELX-Distant (Köksal and Özgür, 2020), displays several unrealistic characteristics, leading to a systematic overestimation of model performance. To alleviate these concerns, we release a new benchmark dataset for the task, named DiS-ReX. We also modify the widely-used bag attention models using an mBERT encoder and provide the first baseline results on the proposed task. We show that DiS-ReX serves as a more challenging dataset than RELX-Distant, leaving ample room for future research in this domain.- Anthology ID:
- 2022.acl-short.95
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 849–863
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.95
- DOI:
- 10.18653/v1/2022.acl-short.95
- Cite (ACL):
- Abhyuday Bhartiya, Kartikeya Badola, and Mausam .. 2022. DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 849–863, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction (Bhartiya et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.acl-short.95.pdf
- Code
- dair-iitd/DiS-ReX
- Data
- DiS-ReX, RELX