Neural machine translation (NMT) has progressed rapidly over the past several
years, and modern models are able to achieve relatively high quality using only
monolingual text data, an approach dubbed Unsupervised Machine Translation
(UNMT). However, these models still struggle in a variety of ways, including
aspects of translation that for a human are the easiest - for instance,
correctly translating common nouns. This work explores a cheap and abundant
resource to combat this problem: bilingual lexica. We test the efficacy of
bilingual lexica in a real-world set-up, on 200-language translation models
trained on web-crawled text. We present several findings: (1) using lexical
data augmentation, we demonstrate sizable performance gains for unsupervised
translation; (2) we compare several families of data augmentation,
demonstrating that they yield similar improvements, and can be combined for
even greater improvements; (3) we demonstrate the importance of carefully
curated lexica over larger, noisier ones, especially with larger models; and
(4) we compare the efficacy of multilingual lexicon data versus
human-translated parallel data. Finally, we open-source GATITOS (available at
this https URL), a new
multilingual lexicon for 26 low-resource languages, which had the highest
performance among lexica in our experiments.

本文探讨使用双语词汇表在网络爬取的文本训练数据上进行无监督机器翻译的有效性，通过词汇数据增强，我们证明了其对于提升无监督翻译成果的重要性，并开源了 GATITOS，这是一个在 26 种低资源语言中表现最好的多语种词汇表。