Even with the latest developments in deep learning and large-scale language
modeling, the task of machine translation (MT) of low-resource languages
remains a challenge. Neural MT systems can be trained in an unsupervised way
without any translation resources but the quality lags behind, especially in
truly low-resource conditions. We propose a training strategy that relies on
pseudo-parallel sentence pairs mined from monolingual corpora in addition to
synthetic sentence pairs back-translated from monolingual corpora. We
experiment with different training schedules and reach an improvement of up to
14.5 BLEU points (English to Ukrainian) over a baseline trained on
back-translated data only.

即使有深度学习和大规模语言建模的最新发展，对于低资源语言的机器翻译任务仍然是一个挑战。我们提出了一种训练策略，依赖于从单语语料库中挖掘的伪平行句对和从单语语料库中反向翻译的合成句对。我们尝试了不同的训练计划，并在基于仅反向翻译数据训练的基线上实现了高达 14.5 BLEU 分（从英语到乌克兰语）的改进。

通过伪平行数据增强无监督机器翻译

Boosting Unsupervised Machine Translation with Pseudo-Parallel Data

Machine translation (MT) has benefited from using synthetic training data
originating from translating monolingual corpora, a technique known as
backtranslation. Combining backtranslated data from different sources has led
to better results than when using such data in isolation. In this work we
analyse the impact that data translated with rule-based, phrase-based
statistical and neural MT systems has on new MT systems. We use a real-world
low-resource use-case (Basque-to-Spanish in the clinical domain) as well as a
high-resource language pair (German-to-English) to test different scenarios
with backtranslation and employ data selection to optimise the synthetic
corpora. We exploit different data selection strategies in order to reduce the
amount of data used, while at the same time maintaining high-quality MT
systems. We further tune the data selection method by taking into account the
quality of the MT systems used for backtranslation and lexical diversity of the
resulting corpora. Our experiments show that incorporating backtranslated data
from different sources can be beneficial, and that availing of data selection
can yield improved performance.

本文提出了使用不同的机器翻译方法进行回译来生成合成训练语料，并使用数据选择策略来优化其性能，进而提高低资源语言的机器翻译质量。结果表明，这种方法可以有效地提升机器翻译的性能。