Technology drives quality Translation.

Language technology is becoming increas-
ingly important as organizations try to deal with
the explosion of digital content and increasing
demands for localized versions of this content.
Fifteen years ago organizations that published
content in more than ten languages were con-
sidered to be unusual, and those that dealt with
more than 50 could probably have been counted
on one hand. Today, however, it is not uncom-
mon for organizations to produce content in
dozens of languages, and increasing numbers
are now dealing with in excess of 200 languages
to one extent or another.
This large-scale change has driven interest in language tech-
nology because the human-oriented approach that worked well
when FIGS (French, Italian, German and Spanish) was consid-
ered sufficient for international business have difficulty scaling
to deal with 50 or 100 languages. Additional factors driving the
shift include the rising importance of user-generated content
and social media; the need for multilingual business intelli-
gence; and the exponential increase in the volume of content
that has been enabled through digital technologies.
The translation and localization industry has long used tech-
nology in the form of translation memory (TM) and terminol-
ogy management systems, but for a variety of reasons it has not
embraced other forms as readily. Most language technologies
today have been deployed as monolingual applications without
the multilingual support required by translators.
Machine translation (MT) is currently the best-known exam-
ple to the public at large, driven largely by the success of free
services pioneered by AltaVista’s Babelfish and then made truly
mainstream by Google Translate. The translation and localiza-
tion community’s acceptance of MT for production purposes has
been considerably more reluctant and cautious, but even here
it is making significant inroads. This increasing acceptance is
leading to more interest in other types of language tech, such as
grammar checking, personal assistants (such as Siri or Google
Now) or opinion mining.
Considering just MT for the moment, it is no secret that
it has not always lived up to the claims of proponents, some
of whom have been predicting for at least the last 50 years
that near-human translation quality is always just ten to fif-
teen years away. However, in the last decade, more and more
organizations have embraced MT as a pragmatic way to help
meet their translation requirements, often in combination with
human translation and post-editing.
MT is currently at a crux: existing technologies have deliv-
ered great rewards, but their rate of progress has slowed as they
have matured. This is not to say that technologies such as statis-
tical machine translation (SMT) have played out, but rather that
the “easy” gains in productivity and quality have been made
and further improvements will require increasing amounts of
effort. Many of the advances in recent years have come from
combining various approaches and tools to take advantage of.

Auteur : aboulabasstranslations

We provide top quality #Interpreting & #Translation Services aboulabass77@gmail.com +221775237850

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