Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we contribute to this question by analyzing cross-lingual transfer for 263 languages from a wide variety of language families. Moreover, we include three popular NLP tasks…
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