@InProceedings{nguyen-chiang:2017:I17-2,
  author    = {Nguyen, Toan Q.  and  Chiang, David},
  title     = {Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {296--301},
  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.},
  url       = {http://www.aclweb.org/anthology/I17-2050}
}