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Machine learning has brought striking advances in multilingual naturallanguage processing capabilities over the past year. For example, the latesttechniques have improved the state-of-the-art performance on the XTREMEmultilingual benchmark by more than 13 points. While a sizeable gap tohuman-level performance remains, improvements have been easier to achieve insome tasks than in others. This paper analyzes the current state ofcross-lingual transfer learning and summarizes some lessons learned. In orderto catalyze meaningful progress, we extend XTREME to XTREME-R, which consistsof an improved set of ten natural language understanding tasks, includingchallenging language-agnostic retrieval tasks, and covers 50 typologicallydiverse languages. In addition, we provide a massively multilingual diagnosticsuite (MultiCheckList) and fine-grained multi-dataset evaluation capabilitiesthrough an interactive public leaderboard to gain a better understanding ofsuch models. The leaderboard and code for XTREME-R will be made available athttps://sites.research.google/xtreme andhttps://github.com/google-research/xtreme respectively.

XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation