Title: Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages,
Abstract: In this paper, we propose a novel and elegant 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) by comparing against existing methods for MSNMT. We also
provide some insights on how the NMT system leverages multilingual information
in such a scenario by visualizing attention.