  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
