  Transformers are responsible for the vast majority of recent advances in
natural language processing. The majority of practical natural language
processing applications of these models are typically enabled through transfer
learning. This paper studies if corpus-specific tokenization used for
fine-tuning improves the resulting performance of the model. Through a series
of experiments, we demonstrate that such tokenization combined with the
initialization and fine-tuning strategy for the vocabulary tokens speeds up the
transfer and boosts the performance of the fine-tuned model. We call this
aspect of transfer facilitation vocabulary transfer.
