  We present a survey on multilingual neural machine translation (MNMT), which
has gained a lot of traction in the recent years. MNMT has been useful in
improving translation quality as a result of knowledge transfer. MNMT is more
promising and interesting than its statistical machine translation counterpart
because end-to-end modeling and distributed representations open new avenues.
Many approaches have been proposed in order to exploit multilingual parallel
corpora for improving translation quality. However, the lack of a comprehensive
survey makes it difficult to determine which approaches are promising and hence
deserve further exploration. In this paper, we present an in-depth survey of
existing literature on MNMT. We categorize various approaches based on the
resource scenarios as well as underlying modeling principles. We hope this
paper will serve as a starting point for researchers and engineers interested
in MNMT.
