Application of English to Chinese Translation Using Word2Vector with Bidirectional Long Short-Term Memory

Abstract

In a procedure of English to Chinese translation, a conventional interactive English translation system is lack of understandable in the semantic framework. However, conventional translation approach does not attain an optimal English to Chinses translation solution as well as translation performance is less. With the aim of enhancing the performance of the English to Chinese machine translation, this research integrates the Word-2-Vector with the Bidirectional Long Short-Term Memory (W2V-BiLSTM) is proposed for the translation of English to Chinese language. Initially, this research utilizes the University of Macau (UM)-Corpus and Workshop on Machine Translation (WMT) datasets are utilized for estimated the model effectiveness. The Word Segmentation method is performed to segment the features and Term Frequency-Inverse Document Frequency (TF-IDF) approach is utilized for the extraction of the language features. Then, the W2V-BiLSTM is utilized for the translation of English to the Chinese Language. The experimental results illustrates that the proposed W2V-BiLSTM approach attains better precision of 0.91 respectively as compared to LSTM.