Oolong: Investigating What Makes Crosslingual Transfer Hard with Controlled Studies
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Little is known about what makes cross-lingual transfer hard, since factors like tokenization, morphology, and syntax all change at once between languages. To disentangle the impact of these factors, we propose a set of controlled transfer studies: we systematically transform GLUE tasks to alter different factors one at a time, then measure the resulting drops in a pretrained model's downstream performance. In contrast to prior work suggesting little effect from syntax on knowledge transfer, we find significant impacts from syntactic shifts (3-6 dataset. However, we find that by far the most impactful factor for crosslingual transfer is the challenge of aligning the new embeddings with the existing transformer layers (18 switching tokenizers (<2 continued pretraining with a small dataset is not very effective at closing this gap - suggesting that new directions are needed for solving this problem.
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