The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification

Abdullatif Köksal, Arzucan Özgür


Abstract
Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations. Creating and annotating a large amount of training data for low-resource languages is impractical and expensive. To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. Our code and data are available at: https://github.com/boun-tabi/RELX
Anthology ID:
2020.findings-emnlp.32
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
340–350
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.32
DOI:
10.18653/v1/2020.findings-emnlp.32
Bibkey:
Cite (ACL):
Abdullatif Köksal and Arzucan Özgür. 2020. The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 340–350, Online. Association for Computational Linguistics.
Cite (Informal):
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (Köksal & Özgür, Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.32.pdf
Code
 boun-tabi/RELX
Data
RELX