Research on high-performance English translation based on topic model  

作　　者：Yumin Shen Hongyu Guo 

机构地区：[1]Zhejiang Gongshang University,Hangzhou,310018,China [2]Graduate School of Education,University of Perpetual Help System DALTA,Metro Manila,1740,Philippines

出　　处：《Digital Communications and Networks》2023年第2期505-511,共7页数字通信与网络（英文版）

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).

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 translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In 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 performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.

关 键 词：Machine translation Topic model Statistical machine translation Bilingual word vector RETELLING 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]