  Multilingual language models have recently gained attention as a promising
solution for representing multiple languages in a single model. In this paper,
we propose new criteria to evaluate the quality of lexical representation and
vocabulary overlap observed in sub-word tokenizers. Our findings show that the
overlap of vocabulary across languages can be actually detrimental to certain
downstream tasks (POS, dependency tree labeling). In contrast, NER and
sentence-level tasks (cross-lingual retrieval, NLI) benefit from sharing
vocabulary. We also observe that the coverage of the language-specific tokens
in the multilingual vocabulary significantly impacts the word-level tasks. Our
study offers a deeper understanding of the role of tokenizers in multilingual
language models and guidelines for future model developers to choose the most
suitable tokenizer for their specific application before undertaking costly
model pre-training
