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A line of work on Transformer-based language models such as BERT hasattempted to use syntactic inductive bias to enhance the pretraining process,on the theory that building syntactic structure into the training processshould reduce the amount of data needed for training. But such methods areoften tested for high-resource languages such as English. In this work, weinvestigate whether these methods can compensate for data sparseness inlow-resource languages, hypothesizing that they ought to be more effective forlow-resource languages. We experiment with five low-resource languages: Uyghur,Wolof, Maltese, Coptic, and Ancient Greek. We find that these syntacticinductive bias methods produce uneven results in low-resource settings, andprovide surprisingly little benefit in most cases.

Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages?