Improving Language Translation Using the Hidden Markov Model

作     者：Yunpeng Chang Xiaoliang Wang Meihua Xue Yuzhen Liu Frank Jiang 

作者机构：School of computer science and engineeringHunan University of Science and TechnologyXiang411201China School of Info TechnologyDeakin UniversityGeelong3220Australia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第6期

页      面：3921-3931页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：support provided from the Cooperative Education Fund of China Ministry of Education(201702113002 and 201801193119) Hunan Natural Science Foundation(2018JJ2138) Degree and Graduate Education Reform Project of Hunan Province(JG2018B096)are greatly appreciated by the authors. 

主　　题：Translation software hidden Markov model context translation 

摘      要：Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.