scholarly journals English to Kurdish Rule-based Machine Translation System

2018 ◽  
Vol 2 (2) ◽  
pp. 32
Author(s):  
Kanaan Mikael Kaka-Khan
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Translation System ◽  
Simple Sentence ◽  
Rule Based ◽  
Ongoing Effort ◽  
Machine Translation System ◽  
Compound Sentence ◽  
Free Open Source

In this paper we present a machine translation system developed to translate simple English sentences to Kurdish. The system is based on the (apertuim) free open source engine that provides the environment and the required tools to develop a machine translation system. The developed system is used to translate some as simple sentence, compound sentence, phrases and idioms from English to Kurdish. The resulting translation is then evaluated manually for accuracy and completeness compared to the result produced by the popular (inKurdish) English to Kurdish machine translation system. The result shows that our system is more accurate than inkurdish system. This paper contributes towards the ongoing effort to achieve full machine-based translation in general and English to Kurdish machine translation in specific.

scholarly journals Otedama: Fast Rule-Based Pre-Ordering for Machine Translation

2016 ◽  
Vol 106 (1) ◽  
pp. 159-168 ◽  
Author(s):  
Julian Hitschler ◽  
Laura Jehl ◽  
Sariya Karimova ◽  
Mayumi Ohta ◽  
Benjamin Körner ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Training Data ◽  
Translation System ◽  
Rule Based ◽  
Machine Translation System ◽  
Target Languages ◽  
Established Technique

Abstract We present Otedama, a fast, open-source tool for rule-based syntactic pre-ordering, a well established technique in statistical machine translation. Otedama implements both a learner for pre-ordering rules, as well as a component for applying these rules to parsed sentences. Our system is compatible with several external parsers and capable of accommodating many source and all target languages in any machine translation paradigm which uses parallel training data. We demonstrate improvements on a patent translation task over a state-of-the-art English-Japanese hierarchical phrase-based machine translation system. We compare Otedama with an existing syntax-based pre-ordering system, showing comparable translation performance at a runtime speedup of a factor of 4.5-10.

scholarly journals Vélþýðingar á íslensku og Apertium-þýðingarkerfið

Orð og tunga ◽  
2016 ◽  
Vol 18 ◽  
pp. 131-143
Author(s):  
Ingibjörg Elsa Björnsdóttir
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Rapid Development ◽  
Translation System ◽  
Rule Based ◽  
Language Technology ◽  
Translation Rule ◽  
Machine Translation System

There has been rapid development in language technology and machine translation in recent decades. There are three main types of machine translation: statistical ma-chine translation, rule-based machine translation, and example-based machine translation. In this article the Apertium machine translation system is discussed in particular. While Apertium was originally designed to translate between closely related languages, it can now handle languages that are much more different and variable in structure. Anyone can participate in the development of the Apertium system since it is an open source soft ware. Thus Apertium is one of the best options available in order to research and develop a machine translation system for Icelandic. The Apertium system has an easy-to-use interface, and it translates almost instantly from Icelandic into English or Swedish. However, the system still has certain limitations as regards vocabulary and ambiguity.

Matxin, an open-source rule-based machine translation system for Basque

2011 ◽  
Vol 25 (1) ◽  
pp. 53-82 ◽  
Author(s):  
Aingeru Mayor ◽  
Iñaki Alegria ◽  
Arantza Díaz de Ilarraza ◽  
Gorka Labaka ◽  
Mikel Lersundi ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Translation System ◽  
Rule Based ◽  
Machine Translation System

OpenMaTrEx: A Free/Open-Source Marker-Driven Example-Based Machine Translation System

2010 ◽  
pp. 121-126 ◽  
Author(s):  
Sandipan Dandapat ◽  
Mikel L. Forcada ◽  
Declan Groves ◽  
Sergio Penkale ◽  
John Tinsley ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Translation System ◽  
Machine Translation System ◽  
Free Open Source

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.

scholarly journals Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

2021 ◽  
Author(s):  
Tanmai Khanna ◽  
Jonathan N. Washington ◽  
Francis M. Tyers ◽  
Sevilay Bayatlı ◽  
Daniel G. Swanson ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Lexical Selection ◽  
Rule Based ◽  
Low Resource ◽  
Language Technology ◽  
Language Data ◽  
Recursive Structures ◽  
Platform Translation ◽  
Free Open Source

AbstractThis paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

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%.

Apertium: a free/open-source platform for rule-based machine translation

2011 ◽  
Vol 25 (2) ◽  
pp. 127-144 ◽  
Author(s):  
Mikel L. Forcada ◽  
Mireia Ginestí-Rosell ◽  
Jacob Nordfalk ◽  
Jim O’Regan ◽  
Sergio Ortiz-Rojas ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Rule Based ◽  
Free Open Source

scholarly journals Joshua 6: A phrase-based and hierarchical statistical machine translation system

2015 ◽  
Vol 104 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Matt Post ◽  
Yuan Cao ◽  
Gaurav Kumar
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Large Scale ◽  
Statistical Machine Translation ◽  
End Users ◽  
Translation System ◽  
Tight Coupling ◽  
Single Function ◽  
Black Boxes ◽  
Machine Translation System

Abstract We describe the version six release of Joshua, an open-source statistical machine translation toolkit. The main difference from release five is the introduction of a simple, unlexicalized, phrase-based stack decoder. This phrase-based decoder shares a hypergraph format with the syntax-based systems, permitting a tight coupling with the existing codebase of feature functions and hypergraph tools. Joshua 6 also includes a number of large-scale discriminative tuners and a simplified sparse feature function interface with reflection-based loading, which allows new features to be used by writing a single function. Finally, Joshua includes a number of simplifications and improvements focused on usability for both researchers and end-users, including the release of language packs — precompiled models that can be run as black boxes.

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