The assessment of translation accuracy of the Lexica Machine Translation System

2003 ◽  
Vol 21 (4) ◽  
pp. 295-306 ◽  
Author(s):  
FPJ Snyman ◽  
JA Naudé
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Machine Translation System ◽  
Translation Accuracy

English to Hindi Machine Translation System in the Context of Homoeopathy Literature

2016 ◽  
Vol 6 (1) ◽  
pp. 46-62
Author(s):  
Pramod P. Sukhadeve
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Simple Complex ◽  
Rule Based ◽  
Machine Translation System ◽  
Translation Accuracy

Over the years, researches in machine translation (MT) systems have gain momentum due to their widespread applicability. A number of systems have come up doing the task successfully for different language pairs. However, to the best of the author's knowledge, no significant work has been done in clinical and medical related domain especially in Homoeopathy. This paper describes a rule based English-Hindi MT system for Homoeopathic sentences. It has been designed to translate a variety of sentences from Homoeopathic literature. To achieve the task, the author developed English and Hindi Homoeopathic corpuses presently having the size 21096 and 23145 sentences respectively. For translation, the input sentences (in English) have been categorised in four different type's i.e. simple, complex, interrogative and ambiguous sentences. The authors tested the translation accuracy using BLEU score. At present, the overall Bleu score of the system is 0.7808 and the accuracy percentage is 82.25%.

scholarly journals Hybrid Translation with Classification: Revisiting Rule-Based and Neural Machine Translation

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 201
Author(s):  
Jin-Xia Huang ◽  
Kyung-Soon Lee ◽  
Young-Kil Kim
Keyword(s):  
Machine Translation ◽  
Classification Accuracy ◽  
Training Data ◽  
Translation System ◽  
Rule Based ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Text Classifiers ◽  
Hybrid Machine Translation ◽  
Translation Accuracy

This paper proposes a hybrid machine-translation system that combines neural machine translation with well-developed rule-based machine translation to utilize the stability of the latter to compensate for the inadequacy of neural machine translation in rare-resource domains. A classifier is introduced to predict which translation from the two systems is more reliable. We explore a set of features that reflect the reliability of translation and its process, and training data is automatically expanded with a small, human-labeled dataset to solve the insufficient-data problem. A series of experiments shows that the hybrid system’s translation accuracy is improved, especially in out-of-domain translations, and classification accuracy is greatly improved when using the proposed features and the automatically constructed training set. A comparison between feature- and text-based classification is also performed, and the results show that the feature-based model achieves better classification accuracy, even when compared to neural network text classifiers.

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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