We incorporate an explicit neural interlingua into a multilingual
encoder-decoder neural machine translation (NMT) architecture. We demonstrate
that our model learns a language-independent representation by performing
direct zero-shot translation (without using pivot translation), and by using
the source sentence embeddings to create an English Yelp review classifier
that, through the mediation of the neural interlingua, can also classify French
and German reviews. Furthermore, we show that, despite using a smaller number
of parameters than a pairwise collection of bilingual NMT models, our approach
produces comparable BLEU scores for each language pair in WMT15.

本研究将显式神经间语纳入多语言编码 - 解码神经机器翻译（NMT）体系结构中，证明该模型通过直接零 - shot 翻译（不使用中转翻译）并使用源语句嵌入来创建英语 Yelp 评论分类器，该分类器能够通过神经间语协调法也对法语和德语评论进行分类，并且即使我们使用的参数数量比成对的 NMT 模型集合少，但我们的方法对于 WMT15 中的每个语言对产生了相当的 BLEU 得分。