The basic idea is to augment the MTL training objective with additional terms, so that the identity of a task cannot be predicted from its data items by the representations resulted from the shared encoder/decoder RNN layers.
nMT is notorious for its need for large amounts of bilingual data (Koehn and Knowles, 2017) to achieve reasonable translation quality.