Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model. (arXiv:2205.15544v2 [cs.CL] UPDATED)

Numerous recent work on unsupervised machine translation (UMT) implies that
competent unsupervised translations of low-resource and unrelated languages,
such as Nepali or Sinhala, are only possible if the model is trained in a
massive multilingual environment, where theses low-resource languages are mixed
with high-resource counterparts. Nonetheless, while the high-resource languages
greatly help kick-start the target low-resource translation tasks, the language
discrepancy between them may hinder their further improvement. In this work, we
propose a simple refinement procedure to disentangle languages …

arxiv language translation unsupervised