Authoring and Documentation Workflow Tools for Haitian Creole: A Minority Language

Although research has been conducted by several institutes on how to process written text for minority and vernacular languages, no academic research project thus far seems to have produced a usable, functional, authoring or translation tool for end-user native speakers of these types of languages. On the other hand, a set of software programs has been in the making for twenty years outside of academia.

Mason, Marilyn. TC-FORUM (2000). Articles>Language>Localization>Machine Translation

Babel Not: Machine Translation for the Technical Communicator

Machine Translation, though useful in certain cases, is still not, and may never be the one-size-fits-all solution for translation needs. Any translation used for commercial or professional purposes must be at the very least checked and double-checked by human translators.

WTB Language Group (2005). Articles>Language>Localization>Machine Translation

Babelfish: Real-Time Machine Translation on the Internet

On December 9, 1997, Digital Equipment Corporation and SYSTRAN A.G. launched AltaVista Translation Service, the first European language translation service for Web content. For the first time, non-English speaking users can translate information on the predominantly English speaking Web in real time.

Ament, Kurt. TC-FORUM (1998). Articles>Language>Localization>Machine Translation

Caterpillar Technical English and Automatic Machine Translation  

Caterpillar is developing an Automatic Machine Translation (AMT) system for translating product support literature into eleven languages. Source language authors write in Caterpillar Technical English (CTE) which uses a constrained English language domain and sophisticated spelling, lexical, grammar and disambiguation software. CTE tools are accessed through pull down menus in the author's text editor. Integrating the source language author into the translations process using CTE has allowed the development of the AMT system which completely eliminates the need for human post-editing of the translated service literature.

Gallup, Sharlene. STC Proceedings (1993). Articles>Language>Translation>Machine Translation

The European Association for Machine Translation

The European Association for Machine Translation (EAMT) is an organization that serves the growing community of people interested in MT and translation tools, including users, developers, and researchers of this increasingly viable technology.

EAMT (2005). Articles>Language>Translation>Machine Translation

Fast Online (Machine) Translation - But...?

Even if the attainable quality of automatic translation systems is insufficient under certain conditions, and despite careful preparation of the original text, nevertheless the translation provides a useful basis for a technical translator. The automatic translation greatly simplifies the production of a foreign language text and leads, all in all, to an efficient translation process. For example, the translation of a customer Website with the help of an automatic translation system (i.e. post-edited machine translation) cost us only a third of the time, which we had previously calculated as pure 'manual work'.

Thiele, Ulrich. TC-FORUM (1999). Articles>Language>Localization>Machine Translation

Java Localization with Translation Memory eXchange Standard

One of the main concerns of internationalization consists of separating the main source code from the texts, the labels, the messages and all the other objects related to the specific language in use. This article briefly explain the TMX standard and a simple TMX Java bridge.

Asuni, Nicola. evolt (2005). Articles>Language>Localization>Machine Translation

Linguistic Issues in Authoring for Technology-Enabled Translation  

For many corporations, growing international is almost a must. Obviously, human translators play a key role in this difficult venture. A range of computer tools aimed at expediting the translation process are now being used by translators. However, whether or not translation tools are used, translators feel frustrated and blame technical writers for their flaws. Authoring does not seem to be done with translation in mind, and the linguistic issues to be solved up front are sometimes countless. An efficient, cost-effective, and high-quality translation requires the right combination of ingredients, and proper authoring will have a major impact on the entire process.

Rivas, Juan-Carlos. STC Proceedings (1999). Articles>Language>Localization>Machine Translation

Machine Translation - 2001 Has Already Arrived

The easiest way to cope with existing language barriers undoubtedly is the use of translation programs, electronic helpers that translate texts automatically. However, with high expectations meeting poor quality translation results in the past, press media regularly concluded that users had better learn the language themselves or employ at least a human translator. Yet a closer look at modern machine translation (MT) programs allows a more subtle view.

Bauer, Elisabeth. TC-FORUM (2000). Articles>Language>Localization>Machine Translation

Machine Translation - A New Dimension and What You Can Expect

Instead of 'translation', AltaVista offered me unbelievable junk, evidently, an unedited MT version of American promotional material. The text was unreal, the result of a myth: You click a button and the translation is staring at you. You are in the middle of a jungle.

Klein, Fred. TC-FORUM (1998). Articles>Language>Localization>Machine Translation

Machine Translation and Multilingual Technical Communication  

The demand by the global market for products which have been localized has brought a whole set of issues and concerns to international technical communication. Of particulur interest is the need to translate technical documentation into a number of languages without sacrificing the necessary timeto-market. Old solutions and processes are insufficient. This paper explores some of the computational tools now offered by the machine translation industry for the facilitation of multilingual document translation as modern corporations need it.

Bennett, Winfield Scott. STC Proceedings (1995). Articles>Language>Localization>Machine Translation

Machine Translation and Technical Communcation  

Machine translation, the use of computers for translating between languages, is only now coming of age, just at a time when there is increasing need for such technology. Views of machine translation range from realistic assessments to extravagant statements for and against the technology. The reality is that machine translation can provide high-speed automated quality translation depending on a variety of factors; it is not a panacea for all translation problems. Successful machine translation requires human-computer interaction which promotes the strengths of each. Machine translation has reached a stage at which it can contribute to multilingual technical communication.

Bennett, Winfield Scott. STC Proceedings (1993). Articles>Language>Translation>Machine Translation

Machine Translation Today and Tomorrow  

The field of machine translation (MT) was the pioneer research area in computational linguistics during the 1950s and 1960s. When it began, the assumed goal was the automatic translation of all kinds of documents at a quality equalling that of the best human translators. It became apparent very soon that this goal was impossible in the foreseeable future.

Hutchins, W. John. Gardez Vlg (2002). Articles>Language>History>Machine Translation

Machine Translation: General Overview  

This chapter introduces the main concepts and methods used for machine translation systems from the beginnings of research in the 1950s until about 1990; it covers the main approaches of rule-based systems (direct, interlingua, transfer, knowledge based), and the principal translation tools; and it concludes with a brief historical sketch.

Hutchins, W. John. Oxford Univ Press (2003). Articles>Language>Localization>Machine Translation

Preparing Your Web Site for Machine Translation

Machine translation is a sophisticated technology. However, it is not as sophisticated as human language. Understanding how MT works on the Web helps designers and developers prepare Web pages for MT. Preparatory tactics improve the usability of MT output.

O'Connell, Theresa A. IBM (2001). Articles>Language>Localization>Machine Translation

SALTing the Alphabet Soup

The language industries are rapidly embracing the use of translation tools such as automatic terminology lookup, terminology mining, terminology consistency checkers, and machine translation. Authoring tools that involve access to a termbase are also appearing, at least in the context of controlled language, but will over time no doubt also be used in the authoring processes where the syntax is less controlled.

Wright, Sue Ellen. TC-FORUM (1999). Articles>Language>Localization>Machine Translation

Should I Use Machine Translation?

Machine translation is somewhat like the child who, when good, was very nice to have around but who, when bad, was just awful. For some guidance in deciding whether machine translation is likely to succeed in your particular environment.

Melby, Alan K. TTT (1995). Articles>Language>Translation>Machine Translation

Towards a Definition of Example-Based Machine Translation  

The example-based approach to MT is becoming increasingly popular. However, such is the variety of techniques and methods used that it is difficult to discern the overall conception of what example- based machine translation (EBMT) is and/or what its practitioners conceive it to be. Although definitions of MT systems are notoriously complex, an attempt is made to define EBMT in contrast to other MT architectures (RBMT and SMT).

Hutchins, W. John. IAMT (2005). Articles>Language>Localization>Machine Translation

Translation Memory 2004: Room to Grow

In 2002, LISA examined Translation Memory (TM) usage and trends. In 2004, LISA returned to the subject with an expanded survey that revisited the questions of 2002 and added questions on specific tool use and interest in new developments of TM. The report, The LISA 2004 Translation Memory Survey, is available free of charge from the LISA website. In this interview, we asked Arle Lommel, LISA’s Publications Manager and author of the survey report, about the significance of the findings and what they mean for the industry.

Lommel, Arle. LISA (2004). Articles>Language>Localization>Machine Translation

Two Years Later: The Triumphs, Trials and Tribulations of Life

Looking at escalating costs and short deadlines for foreign-language documentation, we decided over two years ago that the time had come for a hands-on study of translation tools and their practical benefits. Machine-translation systems such as Systran and Logos were not an option; instead, we directed our attention toward Translation Memory tools.

Kreitmeier, Peter. TC-FORUM (1998). Articles>Language>Localization>Machine Translation

An Unfair Test  

Machine translation has improved greatly over the years, but one type of human communication, figurative language, remains difficult for computers to tackle. When we hear 'You can lead a horse to water but you can’t make him drink,' we know that the speaker is probably commenting on human, not equine, nature.

Intercom (2004). Articles>Language>Localization>Machine Translation

Why Can't a Computer Translate More Like a Person?

Translation is difficult, even for people. To begin with, you have to know two languages intimately. And even if you speak two or more languages fluently, it is not a trivial matter to produce a good translation.

Melby, Alan K. TTT (1995). Articles>Language>Translation>Machine Translation

Evaluation of International English and Machine Translation

Machine translation often gives humorous translations or incorrect translations. Usually, a bad translation is because the source text is not clear in a way that a machine can 'understand'. If text is optimised for machine translation, machine translation gives excellent results. There are two sets of texts. The first set is written in standard English. The second set is equivalent to the standard English text, but it is optimised for machine translation. Google Translate was used to translate the texts into Bulgarian and into Spanish.

Unwalla, Mike. TechScribe (2009). Articles>Language>Localization>Machine Translation