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.

本文提出了一种简单的多源神经机器翻译解决方案 —— 通过预处理 N 路多语种语料库，将源语言句子连接成一个长的多源输入句子，不修改神经机器翻译（NMT）架构或训练过程，使用预处理的语料库训练 NMT 系统，实验结果表明该方法在资源匮乏和资源丰富情况下均有效（使用 2 种源语言可达到 4 BLEU，使用 5 种源语言可达到 6 BLEU），同时与现有的 MSNMT 方法进行比较，表明我们的方法尽管简单却具有竞争力的结果。我们还通过可视化关注力提供了一些多语言信息的见解。