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A client recently called to let us know how happy he was with our translation of his Employee Handbook. He’d tried to have it translated in the past but was flabbergasted by the poor quality of the translation he received from a supposedly reputable translation company. After taking a look, I quickly realized that this was a machine translation and proceeded to explain the difference between HUMAN GENERATED translations and MACHINE or COMPUTER ASSISTED translations; that difference being of course, who is doing the translating.

To put it simply, human generated translations are like the In-N-Out of fast food chains. Just like In-N-Out makes their fries on the spot with old fashioned cutters, Reliable Translations translators manually type the document which they are working on word for word. That means that each and every word was generated by a person who is a native speaker of that language.

At this time, there is no computer on earth that even comes close to having the linguistic computation capabilities of an adult human being, which means that any translation that is not human generated will inevitably, be inferior. Here is an article I came across from BBC which sheds some light on machine translations, and why they don’t live up to the hype.

-Cristal Munoz

Linguist Director

LOST IN [MACHINE] TRANSLATION

Scientists have been trying to automatically translate languages for almost as long as computers have been in existence. So why is it so hard?

Earlier this year, the Malaysian Ministry of Defence unveiled its glossy new website, designed to show off its military prowess and high standards to the world. Unfortunately, nobody had bothered to check the English translations.

One section said that the Malaysian government had taken “drastic measures to increase the level of any national security threat” after the country’s independence in 1957. Another page suggested women should not wear items that “poke out the eye”, an apparent translation of a rule that women should not wear revealing clothing.

Initially it was just sniggering Malaysians who passed the gaffes around on social media, but the chortles soon became global, triggering the Defence Minister to admit that the ministry had used the free online tool Google Translate. He subsequently ordered the new military site to be The episode was embarrassing for the Malaysian ministry, but it also provides an object lesson in the limitations of today’s machine translation technology, which despite billions of pounds of research and massive demand from businesses, politicians and the military, not to mention tourists, is still only stuttering along.

According to Phil Blunsom, a lecturer and machine translation researcher at the University of Oxford, the field has made a lot of progress. But a time when a computer can match the interpretive skills of a professional is “still a long way off”.

So why is it so hard to automatically translate texts?

Scientists and academics have been trying to automate translation for almost as long as computers have been in existence. In the 1940s and 1950s it was widely assumed that once the vocabulary and the rules of grammar of a language had been codified, it would make automated translation easy, according to Dr Blunsom. But attempts to make computers learn languages in this way over the next forty years were largely unsuccessful, unless the range of words they were expected to translate was very limited.

“The main problem is that language is too complex,” explains Philipp Koehn, a machine translation researcher at the University of Edinburgh School of Informatics. “Language is always ambiguous, so you can’t always use rules, and new vocabulary is always coming in, so you need someone to continually maintain those rules.” What it boils down to is that there are simply too many possible rules for them all to be written down, and there are also too many exceptions to those rules, he adds.

Then in the 1980s, computer giant IBM carried out pioneering research into the use of words in sentences. Specifically, its researchers examined the relative frequency of different groups of three words occurring in a sentence. For example, they noted “going to go” occurs far more frequently than “going too, go” or “going two go”. So although the three phrases sound almost identical, the first is statistically most likely to be correct.

This apparently simple insight had huge repercussions, opening up a new statistical approach to translation.

“The vast majority of research into machine translation is now pursuing the statistical approach,” says Dr Blunsom. Online services such as Google Translate and Yahoo! Babel Fish both use statistical machine translation techniques – although Yahoo!’s system is best described as a hybrid approach that makes heavy use of rules, as well as statistics.

To view the full Article, please visit: http://www.bbc.com/future/story/20120306-lost-in-machinetranslation/3