Motivated by those outcomes, in this work we explore in more detail the practical feasibility of multilingual and zeroshot approaches.
we explore the benefit of adding training data involving related languages in a zero-shot setting and, in that framework, we compare the behavior of state-of-the-art Transformer and Recurrent NMT models.