Open Access
ARTICLE
A Novel Beam Search to Improve Neural Machine Translation for English-Chinese
Xinyue Lin1, Jin Liu1, *, Jianming Zhang2, Se-Jung Lim3
1 College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China.
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
3 Liberal Arts & Convergence Studies, Honam University, Gwangju, 62399, Korea.
* Corresponding Author: Jin Liu. Email: .
Computers, Materials & Continua 2020, 65(1), 387-404. https://doi.org/10.32604/cmc.2020.010984
Received 12 April 2020; Accepted 12 May 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
X. Lin, J. Liu, J. Zhang and S. Lim, "A novel beam search to improve neural machine translation for english-chinese,"
Computers, Materials & Continua, vol. 65, no.1, pp. 387–404, 2020. https://doi.org/10.32604/cmc.2020.010984
Citations