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Relation extraction (RE) is a fundamental task in information extraction,whose extension to multilingual settings has been hindered by the lack ofsupervised resources comparable in size to large English datasets such asTACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACREDdataset, covering 12 typologically diverse languages from 9 language families,which is created by machine-translating TACRED instances and automaticallyprojecting their entity annotations. We analyze translation and annotationprojection quality, identify error categories, and experimentally evaluatefine-tuned pretrained mono- and multilingual language models in common transferlearning scenarios. Our analyses show that machine translation is a viablestrategy to transfer RE instances, with native speakers judging more than 83%of the translated instances to be linguistically and semantically acceptable.We find monolingual RE model performance to be comparable to the Englishoriginal for many of the target languages, and that multilingual models trainedon a combination of English and target language data can outperform theirmonolingual counterparts. However, we also observe a variety of translation andannotation projection errors, both due to the MT systems and linguisticfeatures of the target languages, such as pronoun-dropping, compounding andinflection, that degrade dataset quality and RE model performance.

MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset