scholarly journals Neural Machine Translation: A Review

2020 ◽  
Vol 69 ◽  
pp. 343-418
Author(s):  
Felix Stahlberg
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Paradigm Shift ◽  
Neural Machine Translation ◽  
Written Text ◽  
Automatic Translation ◽  
Trace Back ◽  
Statistical Mt ◽  
Recent Trends ◽  
Single Neural Network

The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural machine translation (NMT), which tackles translation with a single neural network. In this work we will trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. We will conclude with a short survey of more recent trends in the field.

scholarly journals ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

Molecules ◽  
2017 ◽  
Vol 22 (10) ◽  
pp. 1732 ◽  
Author(s):  
Renzhi Cao ◽  
Colton Freitas ◽  
Leong Chan ◽  
Miao Sun ◽  
Haiqing Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Neural Machine Translation

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

Automatic translation of spoken English based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Lin Lin ◽  
Jie Liu ◽  
Xuebing Zhang ◽  
Xiufang Liang
Keyword(s):  
Machine Translation ◽  
Learning Algorithm ◽  
English Word ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Spoken English ◽  
Automatic Translation ◽  
Study Designs ◽  
Chinese And English

Due to the complexity of English machine translation technology and its broad application prospects, many experts and scholars have invested more energy to analyze it. In view of the complex and changeable English forms, the large difference between Chinese and English word order, and insufficient Chinese-English parallel corpus resources, this paper uses deep learning to complete the conversion between Chinese and English. The research focus of this paper is how to use language pairs with rich parallel corpus resources to improve the performance of Chinese-English neural machine translation, that is, to use multi-task learning to train neural machine translation models. Moreover, this research proposes a low-resource neural machine translation method based on weight sharing, which uses the weight-sharing method to improve the performance of Chinese-English low-resource neural machine translation. In addition, this study designs a control experiment to analyze the effectiveness of this study model. The research results show that the model proposed in this paper has a certain effect.

An Improved English-to-Mizo Neural Machine Translation

2021 ◽  
Vol 20 (4) ◽  
pp. 1-21
Author(s):  
Candy Lalrempuii ◽  
Badal Soni ◽  
Partha Pakray
Keyword(s):  
Machine Translation ◽  
Model Performance ◽  
Sentence Length ◽  
Prediction Errors ◽  
Indian Languages ◽  
Neural Machine Translation ◽  
Automatic Translation ◽  
Translation Techniques ◽  
Language Pair

Machine Translation is an effort to bridge language barriers and misinterpretations, making communication more convenient through the automatic translation of languages. The quality of translations produced by corpus-based approaches predominantly depends on the availability of a large parallel corpus. Although machine translation of many Indian languages has progressively gained attention, there is very limited research on machine translation and the challenges of using various machine translation techniques for a low-resource language such as Mizo. In this article, we have implemented and compared statistical-based approaches with modern neural-based approaches for the English–Mizo language pair. We have experimented with different tokenization methods, architectures, and configurations. The performance of translations predicted by the trained models has been evaluated using automatic and human evaluation measures. Furthermore, we have analyzed the prediction errors of the models and the quality of predictions based on variations in sentence length and compared the model performance with the existing baselines.

scholarly journals Is Neural Machine Translation the New State of the Art?

2017 ◽  
Vol 108 (1) ◽  
pp. 109-120 ◽  
Author(s):  
Sheila Castilho ◽  
Joss Moorkens ◽  
Federico Gaspari ◽  
Iacer Calixto ◽  
John Tinsley ◽  
...  
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Evaluation Methods ◽  
Automatic Evaluation ◽  
New Paradigm ◽  
Neural Machine Translation ◽  
Human Evaluation ◽  
Statistical Mt

Abstract This paper discusses neural machine translation (NMT), a new paradigm in the MT field, comparing the quality of NMT systems with statistical MT by describing three studies using automatic and human evaluation methods. Automatic evaluation results presented for NMT are very promising, however human evaluations show mixed results. We report increases in fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to oversell.

scholarly journals Pengembangan Metode Neural Machine Translation Berdasarkan Hyperparameter Neural Network

2021 ◽  
Vol 2021 (1) ◽  
pp. 935-946
Author(s):  
Muhammad Yusuf Aristyanto ◽  
Robert Kurniawan
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Dropout Rate ◽  
Activation Function ◽  
Batch Size ◽  
Neural Machine Translation

Manusia sebagai makhluk sosial yang selalu ingin berhubungan dengan manusia lainnya memaksa manusia untuk saling berkomunikasi. Di sinilah peran bahasa menjadi amat penting, karena dengan adanya bahasa, maka akan dengan mudah mengerti apa yang ingin disampaikan oleh orang lain. Untuk itu, perlu adanya media yang dapat membantu memahami berbagai bahasa di dunia, salah satunya adalah mesin penerjemah. Salah satu metode yang dapat digunakan untuk membuat mesin penerjemah adalah Neural Machine Translation (NMT). NMT yang sekarang sudah ada masih memiliki berbagai kekurangan dan perlu dilakukan pengembangan lebih jauh. Diantaranya pada masalah overfitting yang membuat modelnya kurang bisa melakukan generalisasi pada data lain yang diujikan. Banyak hal yang mempengaruhi performa dari NMT tersebut, salah satunya adalah ukuran hyperparameter yang digunakan dan arsitektur model yang digunakan. Namun belum ada ukuran pasti yang dapat digunakan untuk menghasilkan model dengan performa yang terbaik. Sehingga penelitian ini bertujuan untuk mengembangkan arsitektur model NMT dan melakukan simulasi pada masing-masing hyperparameter Neural Network dan ukuran pada arsitektur modelnya, antara lain batch size, epoch, optimizer, activation function, dan dropout rate. Hasil yang didapatkan adalah model pengembangan dapat mengatasi masalah overfitting dari model sebelumnya dengan akurasi sebesar 72,24% dan skor BLEU sebesar 45,83% yang dilakukan pada data uji lainnya.

scholarly journals Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment

2021 ◽  
Vol 284 ◽  
pp. 08001
Author(s):  
Ilya Ulitkin ◽  
Irina Filippova ◽  
Natalia Ivanova ◽  
Alexey Poroykov
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Dramatic Improvement ◽  
Automatic Evaluation ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Automatic Translation ◽  
Translation Systems ◽  
Qualitative Changes

We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n-gram models, make it possible to compare the quality of machine translation to reference translation. We employ modern metrics for automatic evaluation of machine translation quality such as BLEU, F-measure, and TER to compare translations made by Google and PROMT neural machine translation systems with translations obtained 5 years ago, when statistical machine translation and rule-based machine translation algorithms were employed by Google and PROMT, respectively, as the main translation algorithms [6]. The evaluation of the translation quality of candidate texts generated by Google and PROMT with reference translation using an automatic translation evaluation program reveal significant qualitative changes as compared with the results obtained 5 years ago, which indicate a dramatic improvement in the work of the above-mentioned online translation systems. Ways to improve the quality of machine translation are discussed. It is shown that modern systems of automatic evaluation of translation quality allow errors made by machine translation systems to be identified and systematized, which will enable the improvement of the quality of translation by these systems in the future.

scholarly journals Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Haidong Ban ◽  
Jing Ning
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Rapid Development ◽  
Model Performance ◽  
Internet Technology ◽  
Target Language ◽  
Translation System ◽  
Improvement Rate ◽  
Translation Model ◽  
Automatic Translation

With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.

scholarly journals A SYSTEMATIC READING IN STATISTICAL TRANSLATION: FROM THE STATISTICAL MACHINE TRANSLATION TO THE NEURAL TRANSLATION MODELS.

2017 ◽  
Author(s):  
Zakaria El Maazouzi ◽  
Badr Eddine EL Mohajir ◽  
Mohammed Al Achhab
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
High Accuracy ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Automatic Translation

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages. They have even made it possible for the translation of some language pairs to approach human translation quality. In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches.  

Neural Machine Translation Using Sequence Modeling

2021 ◽  
Vol 9 (8) ◽  
pp. 2153-2169
Author(s):  
N Revathi
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Term Memory ◽  
Sequence Modeling

Abstract: Language is a main mode of communication, and translation is a critical tool for understanding information in a foreign language. Without the help of human translators, machine translation allows users to absorb unfamiliar linguistic material. The main goal of this project is to create a practical language translation from English to Hindi. Given its relevance and potential in the English-Hindi translation, machine translation is an efficient way to turn content into a new language without employing people. Among all available translation machines, Neural Machine Translation (NMT) is one of the most efficient ways. So, in this case, we're employing Sequence to Sequence Modeling, which includes the Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM), and Encoder-Decoder methods. Deep Neural Network (DNN) comprehension and principles of deep learning, i.e. machine translation, are disclosed in the field of Natural Language Processing (NLP). In machine reclining techniques, DNN plays a crucial role. Keywords: Sequence to Sequence, Encoder-Decoder, Recurrent Neural Network, Long & Short term Memory, Deep Neural Network.

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