Impact of Statistical Language Model on Example Based Machine Translation System between Kazakh and Turkish Languages

2020 ◽  
Author(s):  
Gulshat Kessikbayeva ◽  
Ilyas Cicekli
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Translation System ◽  
Statistical Language Model ◽  
Machine Translation System

A Comparison of Phrase Based and Word based Language Model for Punjabi

2017 ◽  
Vol 7 (7) ◽  
pp. 444
Author(s):  
Umrinderpal Singh
Keyword(s):  
Machine Translation ◽  
Great Improvement ◽  
Language Model ◽  
Language Models ◽  
Translation System ◽  
Information Base ◽  
Model Yield ◽  
Machine Translation System ◽  
N Gram

A language model provides connection to the decoding process to determine a precise word from several available options in the information base or phrase table. The language model can be generated using n-gram approach. Various language models and smoothing procedures are there to determine this model, like unigram, bigram, trigram, interpolation, backoff language model etc. We have done some experiments with different language models where we have used phrases in place of words as the smallest unit. Experiments have shown that phrase based language model yield more accurate results as compared to simple word based mode. We have also done some experiments with machine translation system where we have used phrase based language model rather than word based model and system yield great improvement.

Machine Translation Based on Translation Rules for Processing Natural Language

2014 ◽  
Vol 926-930 ◽  
pp. 2682-2685
Author(s):  
Lan Dong Li ◽  
Wen Ying Xing ◽  
Xue Long Zhang
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Hybrid Approach ◽  
Implementation Strategies ◽  
Translation System ◽  
Translation Speed ◽  
Machine Translation System ◽  
Design Ideas ◽  
Speed And Accuracy ◽  
Base Design

This paper presents a hybrid approach, which integrates an example-pattern-based method and a rule-based method, to the design and implementation of an English-Chinese machine translation system. It focuses discussion on language model , knowledge base, design ideas and implementation strategies. Our system has been tested based on requirement details listed in the Outlines for Automatic Evaluation of Machine Translation constituted by National Hi-Tech Project 863, and compared with the Huajian system. Experiment results indicate that our system has high translation speed and accuracy.

Research on the Machine Translation Based on the Hybrid Approach

2014 ◽  
Vol 687-691 ◽  
pp. 1754-1757
Author(s):  
Shu Tao Zhou
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Hybrid Approach ◽  
Implementation Strategies ◽  
Translation System ◽  
Translation Speed ◽  
Machine Translation System ◽  
Applications Of Machine Learning ◽  
Design Ideas ◽  
Base Design

This paper firstly introduces the recent research on machine translation and describes the hybrid strategies on machine translation in detail, and discusses the applications of machine learning for machine translation. The hybrid approach is a method for translation which integrates an example-pattern-based method and a rule-based method, to the design and implementation of an English-Chinese machine translation system. It focuses discussion on language model, knowledge base, design ideas and implementation strategies. Our system has been tested based on requirement details listed in the Outlines for Automatic Evaluation of Machine Translation constituted by National Hi-Tech Project, and compared with the assigned system. Experiment results indicate that our system has high translation speed and accuracy.

scholarly journals Memory-Based Machine Translation and Language Modeling

2009 ◽  
Vol 91 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Antal van den Bosch ◽  
Peter Berck
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Language Model ◽  
Language Modeling ◽  
Target Language ◽  
Translation System ◽  
Reference Guide ◽  
Fast Training ◽  
Open Source Software Package ◽  
Machine Translation System

Memory-Based Machine Translation and Language Modeling We describe a freely available open source memory-based machine translation system, mbmt. Its translation model is a fast approximate memory-based classifier, trained to map trigrams of source-language words onto trigrams of target-language words. In a second decoding step, the predicted trigrams are rearranged according to their overlap, and candidate output sequences are ranked according to a memory-based language model. We report on the scaling abilities of the memory-based approach, observing fast training and testing times, and linear scaling behavior in speed and memory costs. The system is released as an open source software package1, for which we provide a first reference guide.

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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