  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 translation knowledge transfer
(transfer learning). MNMT is more promising and interesting than its
statistical machine translation counterpart because end-to-end modeling and
distributed representations open new avenues for research on machine
translation. 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 first categorize various
approaches based on their central use-case and then further categorize them
based on resource scenarios, underlying modeling principles, core-issues and
challenges. Wherever possible we address the strengths and weaknesses of
several techniques by comparing them with each other. We also discuss the
future directions that MNMT research might take. This paper is aimed towards
both, beginners and experts in NMT. We hope this paper will serve as a starting
point as well as a source of new ideas for researchers and engineers interested
in MNMT.
