  Prior works have demonstrated that a low-resource language pair can benefit
from multilingual machine translation (MT) systems, which rely on many language
pairs' joint training. This paper proposes two simple strategies to address the
rare word issue in multilingual MT systems for two low-resource language pairs:
French-Vietnamese and English-Vietnamese. The first strategy is about dynamical
learning word similarity of tokens in the shared space among source languages
while another one attempts to augment the translation ability of rare words
through updating their embeddings during the training. Besides, we leverage
monolingual data for multilingual MT systems to increase the amount of
synthetic parallel corpora while dealing with the data sparsity problem. We
have shown significant improvements of up to +1.62 and +2.54 BLEU points over
the bilingual baseline systems for both language pairs and released our
datasets for the research community.
