Abstract
Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.- Anthology ID:
- P17-1004
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–43
- Language:
- URL:
- https://aclanthology.org/P17-1004
- DOI:
- 10.18653/v1/P17-1004
- Cite (ACL):
- Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2017. Neural Relation Extraction with Multi-lingual Attention. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34–43, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Neural Relation Extraction with Multi-lingual Attention (Lin et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/P17-1004.pdf