Effect of African Language Pretraining on XTREME-R Cross-Lingual Transfer Performance

Abstract

Abstract: Recent advances in training multilingual language models on large datasets seem to have shown promising results in knowledge transfer across languages and achieve high performance on downstream tasks. However, we question to what extent the current evaluation benchmarks and setups accurately measure zero-shot cross-lingual knowledge transfer. In this work, we challenge the assumption that high zero-shot performance on target tasks reflects high cross-lingual ability by introducing more challenging setups involving instances with multiple languages. Through extensive experiments and analysis, w

Research Question

What is the effect of incorporating African language-specific pretraining data on the cross-lingual transfer performance of multilingual models in XTREME-R benchmarks?

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