A Novel Beam Search to Improve Neural Machine Translation for English-Chinese  

作　　者：Xinyue Lin Jin Liu Jianming Zhang Se-Jung Lim 

机构地区：[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

出　　处：《Computers, Materials & Continua》2020年第10期387-404,共18页计算机、材料和连续体（英文）

基　　金：This work is supported by the National Natural Science Foundation of China(61872231,61701297).

摘　　要：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.

关 键 词：Neural machine translation beam search reinforcement learning 

分 类 号：H31[语言文字—英语]