@Article{jai.2020.010476,
AUTHOR = {Jinyingming Zhang , Jin Liu, Xinyue Lin},
TITLE = {Improve Neural Machine Translation by Building Word Vector  with Part of Speech},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {79--88},
URL = {http://www.techscience.com/jai/v2n2/39516},
ISSN = {2579-003X},
ABSTRACT = {Neural Machine Translation (NMT) based system is an important technology 
for translation applications. However, there is plenty of rooms for the improvement of 
NMT. In the process of NMT, traditional word vector cannot distinguish the same words 
under different parts of speech (POS). Aiming to alleviate this problem, this paper proposed 
a new word vector training method based on POS feature. It can efficiently improve the 
quality of translation by adding POS feature to the training process of word vectors. In the 
experiments, we conducted extensive experiments to evaluate our methods. The 
experimental result shows that the proposed method is beneficial to improve the quality of 
translation from English into Chinese.},
DOI = {10.32604/jai.2020.010476}
}