Before revision:  To this end, we propose   , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
After revision:  To this end, we propose  , a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.