Open AccessResearch on Tibetan-Chinese neural network machine translation with few samples
Author(s) -
Yong Sun,
Yong Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012095
Subject(s) - machine translation , computer science , artificial intelligence , transformer , natural language processing , artificial neural network , bleu , baseline (sea) , translation (biology) , chinese language , task (project management) , linguistics , engineering , biochemistry , oceanography , chemistry , philosophy , systems engineering , voltage , geology , messenger rna , electrical engineering , gene
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.