Deep learning-based techniques to enhance the precision of phrase-based statistical machine translation system for Indian languages

2020 ◽  
Vol 13 (1/2) ◽  
pp. 239
Author(s):  
J.P. Sanjanasri ◽  
M. Anand Kumar ◽  
K.P. Soman
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Indian Languages ◽  
Machine Translation System

scholarly journals Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

2016 ◽  
Vol 6 (3) ◽  
pp. 1059 ◽  
Author(s):  
K. Jaya ◽  
Deepa Gupta
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Research Work ◽  
Translation System ◽  
Indian Languages ◽  
Translation Quality ◽  
Parallel Data ◽  
Machine Translation System ◽  
Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing  these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work  becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model.  The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language.  This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used.  With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

2016 ◽  
Vol 6 (3) ◽  
pp. 1059 ◽  
Author(s):  
K. Jaya ◽  
Deepa Gupta
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Research Work ◽  
Translation System ◽  
Indian Languages ◽  
Translation Quality ◽  
Parallel Data ◽  
Machine Translation System ◽  
Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing  these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work  becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model.  The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language.  This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used.  With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

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.

scholarly journals An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

2014 ◽  
Vol 102 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Torregrosa Daniel ◽  
Forcada Mikel L. ◽  
Pérez-Ortiz Juan Antonio
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Web Application ◽  
Statistical Machine Translation ◽  
Black Box ◽  
Translation System ◽  
Web Tool ◽  
Web Based ◽  
Strongly Coupled ◽  
Machine Translation System

Abstract We present a web-based open-source tool for interactive translation prediction (ITP) and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.

Translation of Medical Texts using Neural Networks

2016 ◽  
Vol 5 (4) ◽  
pp. 51-66 ◽  
Author(s):  
Krzysztof Wolk ◽  
Krzysztof P. Marasek
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
European Medicines Agency ◽  
Translation System ◽  
Training Methods ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Source Sentence ◽  
Parallel Text ◽  
Translation Systems

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

scholarly journals An Overview of the Shared Task on Machine Translation in Indian Languages (MTIL) – 2017

2019 ◽  
Vol 28 (3) ◽  
pp. 455-464 ◽  
Author(s):  
M. Anand Kumar ◽  
B. Premjith ◽  
Shivkaran Singh ◽  
S. Rajendran ◽  
K. P. Soman
Keyword(s):  
Machine Translation ◽  
The Internet ◽  
Translation System ◽  
Indian Languages ◽  
Shared Task ◽  
European Languages ◽  
Regional Language ◽  
Human Evaluation ◽  
Machine Translation System ◽  
Translation Systems

Abstract In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and other European languages. However, it is still a nascent stage for Indian languages. This paper presents an overview of the Machine Translation in Indian Languages shared task conducted on September 7–8, 2017, at Amrita Vishwa Vidyapeetham, Coimbatore, India. This machine translation shared task in Indian languages is mainly focused on the development of English-Tamil, English-Hindi, English-Malayalam and English-Punjabi language pairs. This shared task aims at the following objectives: (a) to examine the state-of-the-art machine translation systems when translating from English to Indian languages; (b) to investigate the challenges faced in translating between English to Indian languages; (c) to create an open-source parallel corpus for Indian languages, which is lacking. Evaluating machine translation output is another challenging task especially for Indian languages. In this shared task, we have evaluated the participant’s outputs with the help of human annotators. As far as we know, this is the first shared task which depends completely on the human evaluation.

Sign in / Sign up

Export Citation Format

Share Document