@inproceedings{lin-etal-2017-neural,
title = "Neural Relation Extraction with Multi-lingual Attention",
author = "Lin, Yankai and
Liu, Zhiyuan and
Sun, Maosong",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1004",
doi = "10.18653/v1/P17-1004",
pages = "34--43",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Neural Relation Extraction with Multi-lingual Attention
%A Lin, Yankai
%A Liu, Zhiyuan
%A Sun, Maosong
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 jul
%I Association for Computational Linguistics
%C Vancouver, Canada
%F lin-etal-2017-neural
%X 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.
%R 10.18653/v1/P17-1004
%U https://aclanthology.org/P17-1004
%U https://doi.org/10.18653/v1/P17-1004
%P 34-43
Markdown (Informal)
[Neural Relation Extraction with Multi-lingual Attention](https://aclanthology.org/P17-1004) (Lin et al., ACL 2017)
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.