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Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Unsupervised neural machine translation (UNMT) that relies solely on massive
monolingual corpora has achieved remarkable results in several translation
tasks. However, in real-world scenarios, massive monolingual corpora do not
exist for some extremely low-resource languages such as Estonian, and UNMT
systems usually perform poorly when there is not an adequate training corpus
for one language. In this paper, we first define and analyze the unbalanced
training data scenario for UNMT. Based on this scenario, we propose UNMT
self-training mechanisms to train a robust UNMT system and improve its
performance in this case. Experimental results on several language pairs show
that the proposed methods substantially outperform conventional UNMT systems