Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter

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 and/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 analyze cross-lingual transfer for 263 languages from a wide variety of language families.Moreover, we include three popular NLP tasks: POS tagging, dependency parsing, and topic classification.Our findings indicate that the effect of linguistic similarity on transfer performance depends on a range of factors: the NLP task, the (mono-or multilingual) input representations, and the definition of linguistic similarity.