Relation extraction (RE) is a fundamental task in information extraction,
whose extension to multilingual settings has been hindered by the lack of
supervised resources comparable in size to large English datasets such as
TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED
dataset, covering 12 typologically diverse languages from 9 language families,
which is created by machine-translating TACRED instances and automatically
projecting their entity annotations. We analyze translation and annotation
projection quality, identify error categories, and experimentally evaluate
fine-tuned pretrained mono- and multilingual language models in common transfer
learning scenarios. Our analyses show that machine translation is a viable
strategy to transfer RE instances, with native speakers judging more than 84%
of the translated instances to be linguistically and semantically acceptable.
We find monolingual RE model performance to be comparable to the English
original for many of the target languages, and that multilingual models trained
on a combination of English and target language data can outperform their
monolingual counterparts. However, we also observe a variety of translation and
annotation projection errors, both due to the MT systems and linguistic
features of the target languages, such as pronoun-dropping, compounding and
inflection, that degrade dataset quality and RE model performance.

介绍了 MultiTACRED 数据集及其在跨语言关系抽取上的应用，证明了机器翻译在此任务上的可行性以及单 / 多语言模型的性能优势，同时指出了翻译和标注错误对数据集质量和模型性能的影响。