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

Relation classification is one of the key topics in information extraction,\nwhich can be used to construct knowledge bases or to provide useful information\nfor question answering. Current approaches for relation classification are\nmainly focused on the English language and require lots of training data with\nhuman annotations. Creating and annotating a large amount of training data for\nlow-resource languages is impractical and expensive. To overcome this issue, we\npropose two cross-lingual relation classification models: a baseline model\nbased on Multilingual BERT and a new multilingual pretraining setup, which\nsignificantly improves the baseline with distant supervision. For evaluation,\nwe introduce a new public benchmark dataset for cross-lingual relation\nclassification in English, French, German, Spanish, and Turkish, called RELX.\nWe also provide the RELX-Distant dataset, which includes hundreds of thousands\nof sentences with relations from Wikipedia and Wikidata collected by distant\nsupervision for these languages. Our code and data are available at:\nhttps://github.com/boun-tabi/RELX\n