scholarly journals Design of English Translation System Based on Deep Learning

2021 ◽  
Vol 1802 (4) ◽  
pp. 042053
Author(s):  
Yang Ting
Keyword(s):  
Deep Learning ◽  
English Translation ◽  
Translation System

Research on Machine Translation Technology in English Translation Process

2014 ◽  
Vol 687-691 ◽  
pp. 1210-1213
Author(s):  
Ke Tian
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
Development Process ◽  
Translation System ◽  
Translation Process ◽  
Sentence Meaning ◽  
Cultural Exchanges ◽  
Response Measures ◽  
World Economic ◽  
Translation Machine

Translation plays an important role in the world economic and cultural exchanges. Translation is divided into machine translation and human translation, which is complement each other in promoting world economic and social development process. In this paper, Collaborative Translation gets much attention, along with the growth of collaborative translation, English translation technology also towards a new milestone, the characteristics of collaborative translation process and scientific literature are briefly introduced, and collaborative translation technology English Translation applications made a brief explanation. From the perspective of the development of machine translation, comparative analysis of the characteristics of human translation machine translation strengths and weaknesses, and we make relevant response measures and selection criteria translation approach. The specific translation system is analyzed from the perspective of textual and the Collaborative Translation shortcomings, as well as interpretation of collaborative translation features, functions and its impact on the meaning and sentence meaning.

scholarly journals Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangjun Dong ◽  
Youchao Yang ◽  
Qiankun Zhang
Keyword(s):  
Feature Extraction ◽  
English Translation ◽  
Selection Process ◽  
Translation System ◽  
Chinese Translation ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Optimal Feature Selection ◽  
Translation Accuracy ◽  
Optimal Feature

In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

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