  The effectiveness of Neural Machine Translation (NMT) models largely depends
on the vocabulary used at training; small vocabularies can lead to
out-of-vocabulary problems -- large ones, to memory issues. Subword (SW)
tokenization has been successfully employed to mitigate these issues. The
choice of vocabulary and SW tokenization has a significant impact on both
training and fine-tuning an NMT model. Fine-tuning is a common practice in
optimizing an MT model with respect to new data. However, new data potentially
introduces new words (or tokens), which, if not taken into consideration, may
lead to suboptimal performance. In addition, the distribution of tokens in the
new data can differ from the distribution of the original data. As such, the
original SW tokenization model could be less suitable for the new data. Through
a systematic empirical evaluation, in this work we compare different strategies
for SW tokenization and vocabulary generation with the ultimate goal to uncover
an optimal setting for fine-tuning a domain-specific model. Furthermore, we
developed several (in-domain) models, the best of which achieves 6 BLEU points
improvement over the baseline.
