Title: Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation,
Abstract: We present a simple method to improve neural translation of a low-resource
language pair using parallel data from a related, also low-resource, language
pair. The method is based on the transfer method of Zoph et al., but whereas
their method ignores any source vocabulary overlap, ours exploits it. First, we
split words using Byte Pair Encoding (BPE) to increase vocabulary overlap.
Then, we train a model on the first language pair and transfer its parameters,
including its source word embeddings, to another model and continue training on
the second language pair. Our experiments show that transfer learning helps
word-based translation only slightly, but when used on top of a much stronger
BPE baseline, it yields larger improvements of up to 4.3 BLEU.