  Multilingual BERT (mBERT) has shown reasonable capability for zero-shot
cross-lingual transfer when fine-tuned on downstream tasks. Since mBERT is not
pre-trained with explicit cross-lingual supervision, transfer performance can
further be improved by aligning mBERT with cross-lingual signal. Prior work
proposes several approaches to align contextualised embeddings. In this paper
we analyse how different forms of cross-lingual supervision and various
alignment methods influence the transfer capability of mBERT in zero-shot
setting. Specifically, we compare parallel corpora vs. dictionary-based
supervision and rotational vs. fine-tuning based alignment methods. We evaluate
the performance of different alignment methodologies across eight languages on
two tasks: Name Entity Recognition and Semantic Slot Filling. In addition, we
propose a novel normalisation method which consistently improves the
performance of rotation-based alignment including a notable 3% F1 improvement
for distant and typologically dissimilar languages. Importantly we identify the
biases of the alignment methods to the type of task and proximity to the
transfer language. We also find that supervision from parallel corpus is
generally superior to dictionary alignments.
