Summary Interpreters and translators convert information from one language into another. Quick Facts: Interpreters and Translators 2015 Median Pay $44,190 per year $21.24 per hour Typical Entry-Level Education Bachelor’s degree Work Experience in a Related Occupation None On-the-job Training Short-term on-the-job training Number of Jobs, 2014 61,000 Job Outlook, 2014-24 29% (Much faster than average) […]
Technology may not replace human translators, but it will help them work better
TALK into your phone in any of the big European languages and a Google app can now turn your words into a foreign language, either in text form or as an electronic voice. Skype, an internet-telephony service, said recently that it would offer much the same (in English and Spanish only). But claims that such technological marvels will spell the end of old-fashioned translation businesses are premature.
Software can give the gist of a foreign tongue, but for business use (if executives are sensible), rough is not enough. And polyglot programs are a pinprick in a vast industry. The business of translation, interpreting and software localisation (revising websites, apps and the like for use in a foreign language) generates revenues of $37 billion a year, reckons Common Sense Advisory (CSA), a consulting firm.
Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.
In the 1950s Machine translation became a reality in research, although references to subject can be found as early as the 17th century. The Georgetown experiment, which involved successful fully automatic translation of more than sixty Russian sentences into English in 1954, was one of the earliest recorded projects. Researchers of the Georgetown experiment asserted their belief that machine translation would be a solved problem within three to five years. In the Soviet Union, similar experiments were performed shortly after. Consequently, the success of the experiment ushered in an era of significant funding for machine translation research in the United States. The achieved progress was much slower than expected; in 1966, the ALPAC report found that ten years of research had not fulfilled the expectations of the Georgetown experiment and resulted in dramatically reduced funding.
Interest grew in statistical models for machine translation, which became more common and also less expensive in the 1980s as available computational power increased.
Although there exists no autonomous system of “fully automatic high quality translation of unrestricted text,” there are many programs now available that are capable of providing useful output within strict constraints. Several of these programs are available online, such as Google Translate and the SYSTRAN system that powers AltaVista’s BabelFish (now Yahoo’s Babelfish as of 9 May 2008).
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