@Article{cmc.2020.010984,
AUTHOR = {Xinyue Lin, Jin Liu, Jianming Zhang, Se-Jung Lim},
TITLE = {A Novel Beam Search to Improve Neural Machine Translation for  English-Chinese},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {387--404},
URL = {http://www.techscience.com/cmc/v65n1/39572},
ISSN = {1546-2226},
ABSTRACT = {Neural Machine Translation (NMT) is an end-to-end learning approach for 
automated translation, overcoming the weaknesses of conventional phrase-based translation 
systems. Although NMT based systems have gained their popularity in commercial 
translation applications, there is still plenty of room for improvement. Being the most 
popular search algorithm in NMT, beam search is vital to the translation result. However, 
traditional beam search can produce duplicate or missing translation due to its target 
sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural 
machine translation improvements based on a novel beam search evaluation function. And 
we use reinforcement learning to train a translation evaluation system to select better 
candidate words for generating translations. In the experiments, we conducted extensive
experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual 
corpora of NiuTrans are used in our experiments. The experiment results prove that the 
proposed methods can effectively improve the English to Chinese translation quality.},
DOI = {10.32604/cmc.2020.010984}
}