  In this paper, we proposed two strategies which can be applied to a
multilingual neural machine translation system in order to better tackle
zero-shot scenarios despite not having any parallel corpus. The experiments
show that they are effective in terms of both performance and computing
resources, especially in multilingual translation of unbalanced data in real
zero-resourced condition when they alleviate the language bias problem.

Computation and Language