A Comparative Study of Omegat and Memsource: Advantages and Disadvantages of Their Primary Features
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Translators' Tool
The Translator’s Tool Box A Computer Primer for Translators by Jost Zetzsche Version 9, December 2010 Copyright © 2010 International Writers’ Group, LLC. All rights reserved. This document, or any part thereof, may not be reproduced or transmitted electronically or by any other means without the prior written permission of International Writers’ Group, LLC. ABBYY FineReader and PDF Transformer are copyrighted by ABBYY Software House. Acrobat, Acrobat Reader, Dreamweaver, FrameMaker, HomeSite, InDesign, Illustrator, PageMaker, Photoshop, and RoboHelp are registered trademarks of Adobe Systems Inc. Acrocheck is copyrighted by acrolinx GmbH. Acronis True Image is a trademark of Acronis, Inc. Across is a trademark of Nero AG. AllChars is copyrighted by Jeroen Laarhoven. ApSIC Xbench and Comparator are copyrighted by ApSIC S.L. Araxis Merge is copyrighted by Araxis Ltd. ASAP Utilities is copyrighted by eGate Internet Solutions. Authoring Memory Tool is copyrighted by Sajan. Belarc Advisor is a trademark of Belarc, Inc. Catalyst and Publisher are trademarks of Alchemy Software Development Ltd. ClipMate is a trademark of Thornsoft Development. ColourProof, ColourTagger, and QA Solution are copyrighted by Yamagata Europe. Complete Word Count is copyrighted by Shauna Kelly. CopyFlow is a trademark of North Atlantic Publishing Systems, Inc. CrossCheck is copyrighted by Global Databases, Ltd. Déjà Vu is a trademark of ATRIL Language Engineering, S.L. Docucom PDF Driver is copyrighted by Zeon Corporation. dtSearch is a trademark of dtSearch Corp. EasyCleaner is a trademark of ToniArts. ExamDiff Pro is a trademark of Prestosoft. EmEditor is copyrighted by Emura Software inc. Error Spy is copyrighted by D.O.G. GmbH. FileHippo is copyrighted by FileHippo.com. -
Workflow Manual
Published on Multilingual Online Translation (http://www.molto-project.eu) D3.3 Translation Tools – Workflow Manual Contract No.: FP7-ICT-247914 Project full title: MOLTO - Multilingual Online Translation Deliverable: D3.3 MOLTO translation tools – workflow manual Security (distribution level): Public Contractual date of delivery: M31 Actual date of delivery: April 2013 Type: Manual Status & version: Final Author(s): Inari Listenmaa, Jussi Rautio Task responsible: UHEL Other contributors: Lauri Alanko, John Camilleri, Thomas Hallgren Abstract Deliverable D3.3 consists of a manual and a description of the workflow of the MOLTO translation tools. The document introduces the components: the open-source translation management system Pootle, the Simple Translation Tool, which supports many different translation methods and the Syntax Editor, which allows to modify text by manipulating abstract syntax trees. The document presents two translation workflows. The first scenario integrates MOLTO tools in a professional translation on fixed source, using Pootle. MOLTO translations with GF grammars are added in machine translation options. Another direction taken is the population of translation memory with GF generated data. In the second workflow, the translator is authorized to do changes to source. The tools used in this scenario are the Simple Translation Tool and the Syntax Editor. 1. Introduction This Deliverable D3.3, is a manual and a description of workflow for the translation tools produced within WP3. As stated in the previous Deliverables 3.2 and 3.1, the user of the translation tools is not required to know how to write GF grammars. They are either translators, whose job is to translate from fixed source, or they are authorized to modify the source text in order to fit into the structures covered by the domain-specific grammar(s). -
Webinar: Translation Memory and Machine Translation
Legal Services National Technology Assistance Project www.lsntap.org Webinar: Translation Memory and Machine Translation Jillian Theil, Claudia Johnson, Diana Glick, Leland Sampson, Maria Mindlin and Sart Rowe Machine Translation The Perils of Google Translate Typically Google translate tool will give you broken language translations. You might be able to tell what the translation is saying but it will be grammatically incorrect and use wrong words. The key when creating something like a sign, is to use more visuals and less words. Arrow signs, people icons, Dollar signs etc. Check for existing resources - look for signs that already exist and you can see what they are doing. Consider using visuals for wayfinding - arrows, etc Conduct Plain language review and editing Ensure your signage is readable (font etc) Back translation is running a translation back into english to ensure it is correct. Case Study: Using chinese as an example: the word for computer mouse is a different word than the animal mouse - so google translate will translate a sentence about this wrong. If possible obtain a legal review of the translation from an attorney / translator. Is it Ever OK to use Google Translate? It’s ok for informal communications, for general understanding or when you are in a complete bind and have no other options. Translation Workflow for Lingotek and People’s Law Library 1. Volunteer contacts them, and they qualify that volunteer 2. The volunteer selects an article to translate and that article is uploaded to lingotek 3. The volunteer performs the actual translation and then the article is assigned to a volunteer reviewer who is a licensed attorney 4. -
Introducing a Cat Tool to Translate: Wordfast
Indonesian Journal of English Language Studies Vol. 2, Number 1, February 2016 Introducing a Cat Tool to Translate: Wordfast Fika Apriliana, Ardiyarso Kurniawan, Sandy Ferianda, and Fidelis Chosa Kastuhandani ABSTRACT This article aims at introducing CAT tools to those prospective translators who are familiar with with the tools for the first time. Some of the CAT tools must be paid for while some others are free. This article is to inform the readers about the list of free and paid CAT tools, advantages and disadvantages of those tools. One does not need special training for using a free CAT tool while using the paid CAT tools, one needs some special preparation. This article is going to focus more on Wordfast Pro as the second most widely used CAT tools after SDLTrados. Wordfast Pro is a paid software the functioning of which is based on the creation of a Translation Memory which facilitates and speeds up the translator's work. This article is going to briefly explain the advantages of Wordfast Pro and the steps of using it. The translation example is presented to reveal the different translation results of Wordfast Pro as a paid CAT tool and OmegaT as a free CAT tool. Therefore, the article will facilitate those who intend to know more about Wordfast Pro and start using it. Keywords: CAT tools, Wordfast Pro, OmegaT INTRODUCTION When using CAT tools, it is Computerized translation has attracted immediately clear which parts of the text the attention of a large number of people must be translated; the unchanging who work directly or indirectly on portions are transferred accurately and translational issues such as professional directly; the time savings due to repeating translators, teachers, linguists, researchers expressions is huge; and expressions are and future translators. -
Omegat 2.1 Manual De Usuario
Acerca de OmegaT ― OmegaT 2.1 Manual de Usuario Acerca de OmegaT OmegaT es una herramienta multiplataforma para la Traducción Asistida por Ordenador, es software libre y cuenta con los siguientes atractivos: Memoria de traducción OmegaT recuerda sus traducciones en una memoria de traducción. Al mismo tiempo, puede utilizar referencias a memorias de traducción anteriores. Las memorias de traducción pueden ser muy útiles para una traducción con una variedad de repeticiones o segmentos de texto razonablemente similares. OmegaT utiliza memorias de traducción para recordar sus traducciones anteriores y sugerirle la traducción más probable para el texto en que está trabajando. Las memorias de traducción pueden ser muy útiles cuando necesita actualizar un documento, que ya ha sido traducido. Sin modificación se seguirá traduciendo y las oraciones actualizadas se muestran con una versión anterior de la misma sentencia que la traducción más probable. Las modificaciones al documento original, por lo tanto, serán tratadas con mayor facilidad. Si está utilizando memorias de traducción creadas previamente, por ejemplo, que le ha asignado la agencia de traducción o su cliente, OmegaT será capaz de utilizarlas como memorias de referencia. OmegaT utiliza el formato de archivo TMX estándar para almacenar y acceder a las memorias de traducción, lo cual garantiza que usted puede intercambiar su material con otras aplicaciones de traducción CAT, que soporten este formato de archivo. Gestión de terminología La gestión de la terminología es importante para la consistencia de la traducción. OmegaT utiliza glosarios que contienen la traducción de palabras sueltas o pequeñas frases, una especie de diccionario bilingüe simplificado para un dominio específico. -
An Empirical Study on the Influence of Translation Suggestions’ Provenance Metadata
An empirical study on the influence of translation suggestions’ provenance metadata A Thesis Submitted for the Degree of Doctor of Philosophy by Lucía Morado Vázquez Department of Computer Science and Information Systems, University of Limerick Supervisor: Dr Chris Exton Co-Supervisor: Reinhard Schäler Submitted to the University of Limerick, August 2012 i Abstract In the area of localisation there is a constant pressure to automate processes in order to reduce the cost and time associated with the ever growing workload. One of the main approaches to achieve this objective is to reuse previously-localised data and metadata using standardised translation memory formats –such as the LISA Translation Memory eXchange (TMX) format or the OASIS XML Localisation Interchange File Format (XLIFF). This research aims to study the effectiveness and importance of the localisation metadata associated with the translation suggestions provided by Computer-Assisted Translation (CAT) tools. Firstly, we analysed the way in which localisation data and metadata can be represented in the current specification of XLIFF (1.2). Secondly, we designed a new format called the Localisation Memory Container (LMC) to organise previously-localised XLIFF files in a single container. Finally, we developed a prototype (XLIFF Phoenix) to leverage the data and metadata from the LMC into untranslated XLIFF files in order to improve the task of the translator by helping CAT tools, not only to produce more translation suggestions easily, but also to enrich those suggestions with relevant metadata. In order to test whether this “CAT-oriented” enriched metadata has any influence in the behaviour of the translator involved in the localisation process, we designed an experimental translation task with translators using a modified CAT tool (Swordfish II). -
Omegat CAT- Aloittelijoille Kirjoittaneet Susan Welsh & Marc Prior Suomentanut Taija Salo 1
OmegaT CAT- aloittelijoille kirjoittaneet Susan Welsh & Marc Prior Suomentanut Taija Salo 1 Tekijänoikeus Tekijänoikeus © 2009 Susan Welsh ja Marc Prior Tätä dokumenttia saa kopioida, levittää ja/tai muokata Free Software Foundationin julkaiseman GNU Free Documentation Licensen version 1.2 tai minkä hyvänsä myöhemmän version mukaisesti; vakiolukuja (”Invariant Sections”), etukansitekstejä (”Front Cover Texts”) ja takakansitekstejä ("Back- Cover Texts") ei saa käyttää. Kopio lisenssistä sisältyy osaan nimeltä "GNU Free Documentation License". Kannen kuva on peräisin osoitteesta www.freeclipartnow.com ja on asetettu vapaasti yleiseen käyttöön. Viimeisin päivitys tehty tammikuussa 2009. Viittaa OmegaT:n versioon 2.0.0. Kuvakaappaukset OmegaT:n versioista 1.6.0 ja 2.0.0. 2 Sisällysluettelo Tekijänoikeus......................................................................................................1 Esittely................................................................................................................3 Kenelle opas on suunnattu.......................................................................3 Mikä on CAT-työkalu, ja mitä hyötyä niistä on?......................................3 1. OpenOffice.orgin lataaminen..........................................................................3 2. OmegaT:n lataaminen.....................................................................................4 3. OmegaT:n asentaminen..................................................................................4 4. OmegaT:n käyttöliittymä................................................................................4 -
Translate's Localization Guide
Translate’s Localization Guide Release 0.9.0 Translate Jun 26, 2020 Contents 1 Localisation Guide 1 2 Glossary 191 3 Language Information 195 i ii CHAPTER 1 Localisation Guide The general aim of this document is not to replace other well written works but to draw them together. So for instance the section on projects contains information that should help you get started and point you to the documents that are often hard to find. The section of translation should provide a general enough overview of common mistakes and pitfalls. We have found the localisation community very fragmented and hope that through this document we can bring people together and unify information that is out there but in many many different places. The one section that we feel is unique is the guide to developers – they make assumptions about localisation without fully understanding the implications, we complain but honestly there is not one place that can help give a developer and overview of what is needed from them, we hope that the developer section goes a long way to solving that issue. 1.1 Purpose The purpose of this document is to provide one reference for localisers. You will find lots of information on localising and packaging on the web but not a single resource that can guide you. Most of the information is also domain specific ie it addresses KDE, Mozilla, etc. We hope that this is more general. This document also goes beyond the technical aspects of localisation which seems to be the domain of other lo- calisation documents. -
Lingo Translation Guide
USER GUIDE MADCAP LINGO 11 Translation Copyright 2019 MadCap Software. All rights reserved. Information in this document is subject to change without notice. The software described in this document is fur- nished under a license agreement or nondisclosure agreement. The software may be used or copied only in accord- ance with the terms of those agreements. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or any means electronic or mechanical, including photocopying and recording for any pur- pose other than the purchaser's personal use without the written permission of MadCap Software. MadCap Software 9191 Towne Center Drive, Suite 150 San Diego, California 92122 858-320-0387 www.madcapsoftware.com THIS PDF WAS CREATED USING MADCAP FLARE. CONTENTS CHAPTER 1 Introduction 6 Types of Content 7 Steps for Translating Files 9 CHAPTER 2 Translation Features 13 Filter 14 Translation Memory 25 Machine Translation 26 Termbases 26 Notes for Segments 27 Adding Numbers from Source Segments 31 Right-Click/Keyboard Shortcut Options 32 Live Preview Mode 34 Inline Formatting and Tags 43 Setting the Translated Status of Files 51 Sending Translated Content for Review 53 CHAPTER 3 Translation Memory 56 Translation Memory Features 58 CONTENTS iii Steps for Using Translation Memory 67 Creating a Translation Memory Database 69 Adding a Translation Memory Database 72 Choosing a Translation Memory Database 73 Statuses and Matches 83 Applying Translation Memory Suggestions 94 Editing a Translation Memory Database -
Beyond Translation Memory: Computers and the Professional Translator Ignacio Garcia, University of Western Sydney
The Journal of Specialised Translation Issue 12 – July 2009 Beyond Translation Memory: Computers and the Professional Translator Ignacio Garcia, University of Western Sydney ABSTRACT Translation has historically been performed by bilinguals equipped with specialised topic knowledge. In the mid 20 th century, textual theory and discourse analysis saw emphasis on a top-down, whole-text approach that paved the way for modern professional translators as linguistic transfer experts. This professionalisation was further driven by the digital revolution in the 90s which caused a huge increase in translation demand, and the creation of purpose-designed translation tools—principally translation memory (TM). However, the same technological processes that briefly empowered the professional translator also signalled a return to a bottom-up approach by concentrating on the segment. Twenty years on, translation tools and workflows continue to narrow this focus, even tending towards simple post-editing of machine translated output. As a result, topic-proficient bilinguals are again entering mainstream translation tasks via simplified translation management processes and crowdsourcing approaches. This article explores these recent trends and predicts that, over the next decade, professional translators will find it increasingly difficult to survive as linguistic transfer experts alone. KEYWORDS Translation, localisation, localization, translation memory, machine translation, professional translation, translation as a utility, hive translation. Introduction The digital age has affected all professions, but change has been felt by translators more keenly than most. Like the rest of the ‘knowledge sector,’ translators are obliged to work on computer screens and do their research using the web. Unlike their colleagues however, they have been propagating this new work environment and fomenting change precisely by their role in translating it. -
Open Architecture for Multilingual Parallel Texts M.T
Open architecture for multilingual parallel texts M.T. Carrasco Benitez Luxembourg, 28 August 2008, version 1 1. Abstract Multilingual parallel texts (abbreviated to parallel texts) are linguistic versions of the same content (“translations”); e.g., the Maastricht Treaty in English and Spanish are parallel texts. This document is about creating an open architecture for the whole Authoring, Translation and Publishing Chain (ATP-chain) for the processing of parallel texts. 2. Summary 2.1. Next steps The next steps should be: • Administrative organisation: create a coordinating organisation and approach the existing organisations that might cooperate; e.g., IETF, LISA, OASIS, W3C. • Public discussion: with an emailing list (or similar) to reach a rough consensus, in particular on aspects such as the required specifications. Organise the necessary meeting(s). • Tools: implement some tools. This might be done simultaneously with the public discussion to better illustrate the approach and support the discussion. 2.2. Best approaches To obtain the best quality, speed and the lowest possible cost (QSC) in the production of parallel texts, one should aim for: • Generating all the linguistic versions ready for publication, from linguistic resources. Probably one of the best approaches. • Seamless ATP-chain implementations. • Authoring: Computer-aided authoring (CAA) tools with a controlled authoring environment; it should deliver source texts prepared for translation. • Translation: Computer-aided translation (CAT) tools to allow translators to focus only in translating and unburden translators from auxiliary tasks such as formatting. These tools should have functionalities such as side-by-side editor and the re-use of previous translations. • Publishing: Computer-aided publishing (CAP) tools to minimise human intervention. -
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation Inaki˜ Alegria, Unai Cabezon, Unai Fernandez de Betono,˜ Gorka Labaka, Aingeru Mayor, Kepa Sarasola and Arkaitz Zubiaga Abstract In this chapter, we define a collaboration framework that enables Wikipe- dia editors to generate new articles while they help development of Machine Trans- lation (MT) systems by providing post-edition logs. This collaboration framework was tested with editors of Basque Wikipedia. Their post-editing of Computer Sci- ence articles has been used to improve the output of a Spanish to Basque MT system called Matxin. For the collaboration between editors and researchers, we selected a set of 100 articles from the Spanish Wikipedia. These articles would then be used as the source texts to be translated into Basque using the MT engine. A group of volunteers from Basque Wikipedia reviewed and corrected the raw MT translations. This collaboration ultimately produced two main benefits: (i) the change logs that would potentially help improve the MT engine by using an automated statistical post-editing system , and (ii) the growth of Basque Wikipedia. The results show that this process can improve the accuracy of an Rule Based MT (RBMT) system in nearly 10% benefiting from the post-edition of 50,000 words in the Computer Inaki˜ Alegria Ixa Group, University of the Basque Country UPV/EHU, e-mail: [email protected] Unai Cabezon Ixa Group, University of the Basque Country, e-mail: [email protected] Unai Fernandez de Betono˜ Basque Wikipedia and University of the Basque Country, e-mail: [email protected] Gorka Labaka Ixa Group, University of the Basque Country, e-mail: [email protected] Aingeru Mayor Ixa Group, University of the Basque Country, e-mail: [email protected] Kepa Sarasola Ixa Group, University of the Basque CountryU, e-mail: [email protected] Arkaitz Zubiaga Basque Wikipedia and Queens College, CUNY, CS Department, Blender Lab, New York, e-mail: [email protected] 1 2 Alegria et al.