Abstract
Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.
Abstract (translated)
无监督神经机器翻译（NMT）近年来引起了人们的广泛关注。尽管目前最先进的无监督翻译方法通常在类似语言（如英语-德语翻译）之间表现良好，但在远程语言之间表现不佳，因为无监督对齐对远程语言不起作用。在这篇文章中，我们介绍了远程语言的无监督支点翻译，它通过多跳将一种语言翻译成远程语言，并且每个跳上的无监督翻译相对比原始直接翻译容易。我们提出了一种学习路由（LTR）方法来选择源语言和目标语言之间的翻译路径。LTR是在语言对上进行训练的，语言对的最佳翻译路径是可用的，并应用于未知的语言对上进行路径选择。对20种语言和294种远程语言对进行的实验证明了远程语言的无监督轴翻译的优点，以及所提出的LTR路径选择的有效性。具体来说，在最佳情况下，LTR比传统的直接无监督方法提高了5.58个布鲁点。
URL
https://arxiv.org/abs/1906.02461
