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.

Development of a method for assessing the quality of machine translation based on ensemble methods in machine learning

2021 ◽  
Author(s):  
A.V. Kozina ◽  
Yu.S. Belov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Quality Assessment ◽  
Machine Translation ◽  
Translation System ◽  
Human Judgment ◽  
Translation Quality ◽  
Machine Translation System ◽  
Translation Systems

Automatically assessing the quality of machine translation is an important yet challenging task for machine translation research. Translation quality assessment is understood as predicting translation quality without reference to the source text. Translation quality depends on the specific machine translation system and often requires post-editing. Manual editing is a long and expensive process. Since the need to quickly determine the quality of translation increases, its automation is required. In this paper, we propose a quality assessment method based on ensemble supervised machine learning methods. The bilingual corpus WMT 2019 for the EnglishRussian language pair was used as data. The text data volume is 17089 sentences, 85% of the data was used for training, and 15% for testing the model. Linguistic functions extracted from the text in the source and target languages were used as features for training the system, since it is these characteristics that can most accurately characterize the translation in terms of quality. The following tools were used for feature extraction: a free language modeling tool based on SRILM and a Stanford POS Tagger parts of speech tagger. Before training the system, the text was preprocessed. The model was trained using three regression methods: Bagging, Extra Tree, and Random Forest. The algorithms were implemented in the Python programming language using the Scikit learn library. The parameters of the random forest method have been optimized using a grid search. The performance of the model was assessed by the mean absolute error MAE and the root mean square error RMSE, as well as by the Pearsоn coefficient, which determines the correlation with human judgment. Testing was carried out using three machine translation systems: Google and Bing neural systems, Mouses statistical machine translation systems based on phrases and based on syntax. Based on the results of the work, the method of additional trees showed itself best. In addition, for all categories of indicators under consideration, the best results are achieved using the Google machine translation system. The developed method showed good results close to human judgment. The system can be used for further research in the task of assessing the quality of translation.

scholarly journals A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qianyu Cao ◽  
Hanmei Hao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Machine Translation ◽  
Big Data Analysis ◽  
Translation System ◽  
Source Text ◽  
Chaotic Neural Network ◽  
Passive Voice ◽  
Machine Translation System

In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

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 Machine Translation of Concise State Sentences for Quantifier Phrases

2019 ◽  
Vol 1 (2) ◽  
pp. p138
Author(s):  
Wu Min
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Deep Neural Network ◽  
Spoken Language ◽  
High Accuracy ◽  
Translation System ◽  
Machine Translation System ◽  
Quantifier Phrases ◽  
Translation Systems ◽  
Very High

With the development of deep learning methods, the machine translation system based on deep neural network has reached a very high accuracy, but for some daily Chinese phenomenon machine translation system is still not able to translate correctly. In this paper, we study a sentence that often appears in Chinese spoken language, that is, a simple state sentence composed of quantitative phrases, and improve the existing machine translation system. The external helper program constructed in this paper is compatible with the current mainstream network translation systems, greatly improving the translation effect of these translation systems on the concise state sentences composed of quantitative phrases.

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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