  Multilingual pretrained models are effective for machine translation and
cross-lingual processing because they contain multiple languages in one model.
However, they are pretrained after their tokenizers are fixed; therefore it is
difficult to change the vocabulary after pretraining. When we extend the
pretrained models to new languages, we must modify the tokenizers
simultaneously. In this paper, we add new subwords to the SentencePiece
tokenizer to apply a multilingual pretrained model to new languages (Inuktitut
in this paper). In our experiments, we segmented Inuktitut sentences into
subwords without changing the segmentation of already pretrained languages, and
applied the mBART-50 pretrained model to English-Inuktitut translation.
