In this paper, we propose a multi-task learning (MTL) framework to effectively utilize monolingual data for MNMT.
the model is jointly trained with translation task on multilingual parallel data and two auxiliary tasks: masked language modeling (MLM) and denoising autoencoding (DAE) on the source-side and target-side monolingual data respectively.