@inproceedings{abdou-etal-2019-x,
title = "{X}-{W}iki{RE}: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension",
author = "Abdou, Mostafa and
Sas, Cezar and
Aralikatte, Rahul and
Augenstein, Isabelle and
S{\o}gaard, Anders",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6130",
doi = "10.18653/v1/D19-6130",
pages = "265--274",
abstract = "Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.",
}
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%0 Conference Proceedings
%T X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
%A Abdou, Mostafa
%A Sas, Cezar
%A Aralikatte, Rahul
%A Augenstein, Isabelle
%A Søgaard, Anders
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F abdou-etal-2019-x
%X Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.
%R 10.18653/v1/D19-6130
%U https://aclanthology.org/D19-6130
%U https://doi.org/10.18653/v1/D19-6130
%P 265-274
Markdown (Informal)
[X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension](https://aclanthology.org/D19-6130) (Abdou et al., 2019)
ACL