Research on Tibetan-Chinese neural network machine translation with few samples

Abstract

Abstract Machine translation is an important task in natural language processing, and the study of Tibetan-Chinese neural machine translation is of profound significance in promoting Tibetan-Chinese scientific and cultural exchanges and the development of education and culture. In this paper, we investigate the performance of these techniques and methods on Tibetan-Chinese NMT with few samples by using deactivated word lists, data augmentation (back translation), pre-training models (ELMO), and attention mechanisms for the techniques and methods widely used in NMT, using seq2seq and Transformer models as the baseline, and finally, the BLEU value of Tibetan-Chinese NMT is increased from the initial 5.53 to 19.03.