  Unsupervised machine translation, which utilizes unpaired monolingual corpora
as training data, has achieved comparable performance against supervised
machine translation. However, it still suffers from data-scarce domains. To
address this issue, this paper presents a novel meta-learning algorithm for
unsupervised neural machine translation (UNMT) that trains the model to adapt
to another domain by utilizing only a small amount of training data. We assume
that domain-general knowledge is a significant factor in handling data-scarce
domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge
learned from high-resource domains, to boost the performance of low-resource
UNMT. Our model surpasses a transfer learning-based approach by up to 2-4 BLEU
scores. Extensive experimental results show that our proposed algorithm is
pertinent for fast adaptation and consistently outperforms other baseline
models.
