Neural Relation Extraction With Multi-Lingual Attention
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1(2017)
摘要
Relation extraction has been widely used for finding unknown relational facts from the 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 monolingual 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络