mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset

The MS MARCO ranking dataset has been widely used for training deep learning models for IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this type of resource is scarce in other languages than English. In this work we present mMARCO, a multilingual version of the MS MARCO passage ranking dataset comprising 8 languages that was created using machine translation. We evaluated mMARCO by fine-tuning mono and multilingual re-ranking models on it. Experimental results demonstrate that multilingual models fine-tuned on our translated dataset achieve superior effectiveness than models fine-tuned on the original English version alone. Also, our distilled multilingual re-ranker is competitive with non-distilled models while having 5.4 times fewer parameters. The translated datasets as well as fine-tuned models are available at https://github.com/unicamp-dl/mMARCO.git.