Both research and commercial machine translation have so far neglected the
importance of properly handling the spelling, lexical and grammar divergences
occurring among language varieties. Notable cases are standard national
varieties such as Brazilian and European Portuguese, and Canadian and European
French, which popular online machine translation services are not keeping
distinct. We show that an evident side effect of modeling such varieties as
unique classes is the generation of inconsistent translations. In this work, we
investigate the problem of training neural machine translation from English to
specific pairs of language varieties, assuming both labeled and unlabeled
parallel texts, and low-resource conditions. We report experiments from English
to two pairs of dialects, EuropeanBrazilian Portuguese and European-Canadian
French, and two pairs of standardized varieties, Croatian-Serbian and
Indonesian-Malay. We show significant BLEU score improvements over baseline
systems when translation into similar languages is learned as a multilingual
task with shared representations.

通过使用具有共享表示的多语言任务解决英文到特定语言品种的神经机器翻译时可能遇到的问题，并利用标签化和非标签化的平行语料库，以及低资源条件对欧洲巴西葡萄牙语、欧洲 - 加拿大法语、克罗地亚 - 塞尔维亚语以及印度尼西亚 - 马来语这四种语言进行实验。实验结果显示，使用共享表示的多语言方法能够显着提高翻译成相似语言的基线系统的 BLEU 分数。