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Why Machine Translation is Not Good Enough

Machine translation that’s good enough to substitute for human interpreters is like the great white whale, sought by science fiction writers, businesses and militaries alike. However, despite all the hype about the latest iPhone translation app and the ubiquity of Google Translate, nobody has yet managed to produce an algorithm that does the job as well as a bilingual human.

A recent article in Slate on the efforts of the US military to develop a machine translation device to substitute for human interpreters in Afghanistan is a case in point. The article describes the results of a 5 year research effort funded by DARPA. The snippet below shows just how well the device performed in place of a Pashto interpreter:

Rachel asked: “Would you introduce me to him?” Aziz failed to understand the machine’s translation (though he does speak English), so she asked again: “Could you introduce me to the village elder?” This time, there was success, after a fashion. Aziz, via the device, replied: “Yes, I can introduce myself to you.”

Unfortunately, Aziz was not the village elder in question. C3PO, where are you when we need you?  DARPA’s speech-to-speech translation system, called TransTac, achieved an 80% accuracy rate by the end of the research project. Obviously, these are not the droids we were looking for.

The problem, as Slate points out, is that computers are great at storing knowledge and making calculations, but they lack the key ingredient of a successful interpreter: understanding. Attempts to add this essential human ingredient by comparing machine translations to human-created translations and by having real people rate translations for quality also tend to fall short. Plus, it’s slow and expensive. As Slate writer Konstantin Kakaes put it:

The difficulty of knowing if a translation is good is not just a technical one: It’s fundamental. The only durable way to judge the faith of a translation is to decide if meaning was conveyed. If you have an algorithm that can make that judgment, you’ve solved a very hard problem indeed.

We couldn’t agree more, and that’s why it’s so important to use trained human translators and interpreters for important communications.

We recently published a further article regarding the pitfalls of MT, you can view it here:

 

The history of Machine Translation

Machine Translation – A Potted History

The concept of machine translation has existed for centuries, but it was not until the early 1950s that it began to become a reality. Since then, machine translation has advanced hugely, though it still cannot yet compete with the skill and finesse that a human mind can apply to translating a document.

The birth of machine translation

In 1949, Warren Weaver of the Rockefeller Foundation put together a set of proposals on how to turn the idea of machine translation into reality. He blended information theory, code breaking lessons learned during the Second World War and the principles of natural language to pave the way for machines to translate one language to another.

One of the earliest machine translation successes was the Georgetown-IBM experiment. In 1954, IBM demonstrated at its New York office a machine that could translate Russian sentences into English. Though the machine could only translate 250 words (into 49 sentences), the world was delighted by the idea. Interest in machine translation around the world saw money being poured into this new field of computer science. The Georgetown experiment researchers, bursting with the confidence of their initial success, predicted that machine translation would be mastered within three to five years. Read more

Skype: Universal Translator

Skype: The Universal Translator

Microsoft  & Skype intend to launch a real time language translator service this year according to an article by the BBC. Satya Nadella, Skype’s chief executive plans to introduce a test version for Windows 8 in the run up to 2015.

Due to increasing competition in the the VOIP market from the likes of google & co, these companies have been forced to pursue & develop more varied product offerings. Should this technology prove effective, it could spell trouble for the interpreting sector in a similar way that machine translation (MT) has impacted the language industry.

Skype claims the service is the result of more than a decade of detailed work with speech recognition systems and believes it could have dramatic concequenceses for the entire communication industry, with Mr Nadella adding

“It is going to make sure you can communicate with anybody without language barriers,”

While Skype’s vice president, Gurdeep Pall, accepts the technology is still very much in its early stages, he believes the prospect of the fabled universal translator is not as far away as it once was

“We’ve invested in speech recognition, automatic translation and machine learning technologies for more than a decade, and now they’re emerging as important components in this more personal computing era,”

“It is early days for this technology, but the Star Trek vision for a Universal Translator isn’t a galaxy away, and its potential is every bit as exciting as those Star Trek examples.”

 

There is still a clear divide between the current abilities of machines vs. humans in regards to quality of translation, as we have covered in previous articles. However, few would deny that gap is narrowing year on year. It has taken MT a while but with the growth in popularity of services like Google translate, a point has been reached where it has undoubtedly begun to muddy the waters of some client perception towards language service providers.

Let us know what you think in the comments, is it an exciting prospect or a worrying development?