scholarly journals An overview of the EtsaTrans machine translation system: compilation of an administrative domain

Literator ◽  
2008 ◽  
Vol 29 (1) ◽  
pp. 231-248
Author(s):  
L. Ehlers
Keyword(s):  
South Africa ◽  
Machine Translation ◽  
Subject Matter ◽  
Translation System ◽  
Language Usage ◽  
Linguistic Performance ◽  
Part Of Speech ◽  
Machine Translation System ◽  
Phases Of Development ◽  
The University

The EtsaTrans machine translation system has been in development at the University of the Free State for the last four years and is currently the only machine translation system being developed in South Africa for specialised and nongeneral translation needs. The purpose of this exposition is to present the program through its phases of development, and to report on current levels of performance. We analyse the output, the size of the database, and then propose the future implementation of a part of speech tagger and word stemmer into the program to improve its linguistic performance. Our goal with the system is not to translate all types of document, but to work in a specialised domain that will allow the system to translate documents that are repetitive in nature. This will enable translators to spend more time on non-repetitive subject matter. By capturing the nature of the language of such repetitive documents in the database, we are able to create a standardised language usage for the specialised domain.

scholarly journals Quantum Neural Network Based Machine Translator for Hindi to English

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ravi Narayan ◽  
V. P. Singh ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Translation System ◽  
Quantum Neural Network ◽  
Individual Word ◽  
Part Of Speech ◽  
Machine Translation System ◽  
Simulation And Evaluation

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

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