Low-resource machine translation is challenged by lacking parallel sentence pairs.We address the specific low-resource machine translation issue from Indonesian to Chinese,and proposes a data augmentation method based on a cognate corpus.Specifically,we optimize the neural machine translation(NMT)model by mixing a cognate corpus,which is mainly derived from the morphological similarity and semantic equivalence between the cognate languages.Experiments demonstrate that the proposed method achieves more than 3 points of the BLEU4 score in the Indonesian-Chinese machine translation.
维普
万方数据