  In this paper, we explore a simple solution to "Multi-Source Neural Machine
Translation" (MSNMT) which only relies on preprocessing a N-way multilingual
corpus without modifying the Neural Machine Translation (NMT) architecture or
training procedure. We simply concatenate the source sentences to form a single
long multi-source input sentence while keeping the target side sentence as it
is and train an NMT system using this preprocessed corpus. We evaluate our
method in resource poor as well as resource rich settings and show its
effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using 5
source languages). We also compare against existing methods for MSNMT and show
that our solution gives competitive results despite its simplicity. We also
provide some insights on how the NMT system leverages multilingual information
in such a scenario by visualizing attention.
