  Multilingual BERT (M-BERT) has been a huge success in both supervised and
zero-shot cross-lingual transfer learning. However, this success has focused
only on the top 104 languages in Wikipedia that it was trained on. In this
paper, we propose a simple but effective approach to extend M-BERT (E-BERT) so
that it can benefit any new language, and show that our approach benefits
languages that are already in M-BERT as well. We perform an extensive set of
experiments with Named Entity Recognition (NER) on 27 languages, only 16 of
which are in M-BERT, and show an average increase of about 6% F1 on languages
that are already in M-BERT and 23% F1 increase on new languages.
