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X-WikiRE : A Large, Multilingual Resource for Relation Extraction as Machine Comprehension. / Abdou, Mostafa; Sas, Cezar; Aralikatte, Rahul; Augenstein, Isabelle; Søgaard, Anders.
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics, 2019. p. 265-274.
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Abdou, M, Sas, C, Aralikatte, R
, Augenstein, I & Søgaard, A 2019,
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension. in
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics, pp. 265-274, 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019),
01/11/2019.
https://doi.org/10.18653/v1/D19-6130APA
Abdou, M., Sas, C., Aralikatte, R.
, Augenstein, I., & Søgaard, A. (2019).
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension. In
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (pp. 265-274). Association for Computational Linguistics.
https://doi.org/10.18653/v1/D19-6130Vancouver
Abdou M, Sas C, Aralikatte R
, Augenstein I, Søgaard A.
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics. 2019. p. 265-274
https://doi.org/10.18653/v1/D19-6130Author
Abdou, Mostafa ; Sas, Cezar ; Aralikatte, Rahul ; Augenstein, Isabelle ; Søgaard, Anders. / X-WikiRE : A Large, Multilingual Resource for Relation Extraction as Machine Comprehension. Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics, 2019. pp. 265-274
Bibtex
@inproceedings{c06295dee8b44e7bbbaa22e548e17b09,
title = "X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension",
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.",
author = "Mostafa Abdou and Cezar Sas and Rahul Aralikatte and Isabelle Augenstein and Anders S{\o}gaard",
year = "2019",
doi = "10.18653/v1/D19-6130",
language = "English",
pages = "265--274",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
publisher = "Association for Computational Linguistics",
note = "2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) ; Conference date: 01-11-2019 Through 01-11-2019",
}
RIS
TY - GEN
T1 - X-WikiRE
T2 - 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
AU - Abdou, Mostafa
AU - Sas, Cezar
AU - Aralikatte, Rahul
AU - Augenstein, Isabelle
AU - Søgaard, Anders
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
U2 - 10.18653/v1/D19-6130
DO - 10.18653/v1/D19-6130
M3 - Article in proceedings
SP - 265
EP - 274
BT - Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
PB - Association for Computational Linguistics
Y2 - 1 November 2019 through 1 November 2019
ER -
