  A multilingual tokenizer is a fundamental component of multilingual neural
machine translation. It is trained from a multilingual corpus. Since a skewed
data distribution is considered to be harmful, a sampling strategy is usually
used to balance languages in the corpus. However, few works have systematically
answered how language imbalance in tokenizer training affects downstream
performance. In this work, we analyze how translation performance changes as
the data ratios among languages vary in the tokenizer training corpus. We find
that while relatively better performance is often observed when languages are
more equally sampled, the downstream performance is more robust to language
imbalance than we usually expected. Two features, UNK rate and closeness to the
character level, can warn of poor downstream performance before performing the
task. We also distinguish language sampling for tokenizer training from
sampling for model training and show that the model is more sensitive to the
latter.
