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