We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is back-translation, which is computationally costly and hard to tune. In this paper we propose instead to use denoising adapters, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.

翻译：我们考虑的是多语种、不受监督的机器翻译、笔译和笔译问题,这些语言只有单一语言数据,使用辅助平行语言对口。对于这个问题,迄今为止利用单一语言数据的标准程序是回译,这是计算成本高、难以调和的。在本文中,我们建议除使用预先培训的MBART-50外,再使用拆译适应器、具有分解目标的适配层。除了这种方法的模块性和灵活性外,我们还表明由此产生的翻译与BLEU衡量的反译是平行的,而且允许逐步增加隐性语言。