Neural Machine Translation for Turkish to English Using Deep Learning

2021 ◽  
pp. 3-9
Author(s):  
Fatih Balki ◽  
Hilmi Demirhan ◽  
Salih Sarp
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Neural Machine Translation

scholarly journals Analyses and Modeling of Neural Machine Translation for English-to-Khasi

2020 ◽  
Vol 9 (2) ◽  
pp. 115-118
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Barrier ◽  
Attention Mechanism ◽  
Training Dataset ◽  
Learning Approach ◽  
Neural Machine Translation ◽  
Community Or ◽  
North East

Language barrier is a common issue faced by humans who move from one community or group to another. Statistical machine translation has enabled us to solve this issue to a certain extent, by formulating models to translate text from one language to another. Statistical machine translation has come a long way but they have their limitations in terms of translating words that belongs to an entirely different context that is not available in the training dataset. This has paved way for neural Machine Translation (NMT), a deep learning approach in solving sequence to sequence translation. Khasi is a language popularly spoken in Meghalaya, a north-east state in India. Its wide and unexplored. In this paper we will discuss about the modeling and analyzing of a NMT base model and a NMT model using Attention mechanism for English to Khasi.

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.

scholarly journals Philipp Koehn: Neural Machine Translation

2021 ◽  
Author(s):  
Wandri Jooste ◽  
Rejwanul Haque ◽  
Andy Way
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
New Techniques ◽  
Neural Machine Translation ◽  
Postgraduate Students ◽  
Learning Techniques ◽  
Computational Aspects ◽  
To Come ◽  
Dominant Paradigm

AbstractNeural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs). The book Neural Machine Translation by Philipp Koehn targets a broad range of readers including researchers, scientists, academics, advanced undergraduate or postgraduate students, and users of MT, covering wider topics including fundamental and advanced neural network-based learning techniques and methodologies used to develop NMT systems. The book demonstrates different linguistic and computational aspects in terms of NMT with the latest practices and standards and investigates problems relating to NMT. Having read this book, the reader should be able to formulate, design, implement, critically assess and evaluate some of the fundamental and advanced deep learning techniques and methods used for MT. Koehn himself notes that he was somewhat overtaken by events, as originally this book was envisaged only as a chapter in a revised, extended version of his 2009 book Statistical Machine Translation. However, in the interim, NMT completely overtook this previously dominant paradigm, and this new book is likely to serve as the reference of note for the field for some time to come, despite the fact that new techniques are coming onstream all the time.

scholarly journals Machine learning: Implications for translator education

2017 ◽  
Vol 62 (2) ◽  
Author(s):  
Gary Massey ◽  
Maureen Ehrensberger-Dow
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Translation ◽  
New Technologies ◽  
Added Value ◽  
Neural Machine Translation ◽  
Computer Aided ◽  
Translator Education

AbstractMachines are learning fast, and human translators must keep pace by learning with, from and about them. Deep learning (DL) and neural machine translation (NMT) are set to change the reality of translation and the distributions of tasks. Although theoretical and practical courses on computer-aided and/or machine translation abound, less attention has been paid to DL and NMT in most translation programmes. The challenge for translation education is to give students the knowledge and toolkits to learn when and how to embrace the new technologies, and to exploit how and when the added value of human intuition, creativity and ethics can and should be deployed.

A Review of Neural Machine Translation based on Deep learning techniques

2021 ◽  
Author(s):  
Sonali Sharma ◽  
Manoj Diwakar ◽  
Prabhishek Singh ◽  
Amrendra Tripathi ◽  
Chandrakala Arya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Neural Machine Translation ◽  
Learning Techniques

scholarly journals An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

2019 ◽  
Vol 28 (4) ◽  
pp. 1-29 ◽  
Author(s):  
Michele Tufano ◽  
Cody Watson ◽  
Gabriele Bavota ◽  
Massimiliano Di Penta ◽  
Martin White ◽  
...  
Keyword(s):  
Empirical Study ◽  
Machine Translation ◽  
Neural Machine Translation ◽  
Bug Fixing ◽  
In The Wild
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