Research on high-performance English translation based on topic model

作     者：Yumin Shen Hongyu Guo Yumin Shen;Hongyu Guo

作者机构：Zhejiang Gongshang UniversityHangzhou310018China Graduate School of EducationUniversity of Perpetual Help System DALTAMetro Manila1740Philippines 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第2期

页      面：505-511页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Social Science Fund of China(Youth Program):“A Study of Acceptability of Chinese Government Public Signs in the New Era and the Countermeasures of the English Translation”(No.:13CYY010) the Subject Construction and Management Project of Zhejiang Gongshang University:“Research on the Organic Integration Path of Constructing Ideological and Political Training and Design of Mixed Teaching Platform during Epidemic Period”(No.:XKJS2020007) Ministry of Education IndustryUniversity Cooperative Education Program:“Research on the Construction of Cross-border Logistics Marketing Bilingual Course Integration”(NO.:202102494002) 

主　　题：Machine translation Topic model Statistical machine translation Bilingual word vector Retelling 

摘      要：Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine ***,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and *** order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation *** this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix *** we design a decoder to simplify the decoding *** show that the proposed method can effectively improve the accuracy of translation.