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How does machine translation work on a fundamental level?

How does machine translation work? That’s a question that is often asked whenever common machine translation applications, such as Google Translate, is used to translate text from one language to another.

The wonders and ramifications of machine translation are just beginning to be realized by the general public using the internet. The most common introduction is, of course, through the use of Google Translate.

Regular users of this application over the years since its introduction have no doubt noticed an improvement in the service over when they first tried it. That’s artificial intelligence (AI) at work. AI is used in most of the machine translation programs on the market to continually improve the algorithms ability to ‘learn’ and expand its capabilities.

There will come a day somewhere in the not-so-distant future, where human translators may have to think about finding a different line of work. That’s how much machine translation has improved with the addition of AI.

But to answer the question of how does machine translation work on a fundamental level, we’ll use the visual example used by most researchers and computer scientists involved in machine translation.

Pyramid Visualization of Machine Translation

Picture an equilateral triangle with the pointy end at the top and the wide end at the bottom. The left side of the triangle represents the source language (original language), and the right side represents the target language you want the words translated into.

If you draw a series of horizontal lines from the top to the bottom of the interior of the pyramid, those tiers will represent the different processes involved in machine translation. The narrow processes at the top of the pyramid represent the simplest processes and the processes become wider (more complicated) the further down the pyramid you descend. 

The beauty of the machine translation algorithm is that it doesn’t perform any more work than it has to. For simple sentence translations between two similar romantic languages, for instance, the translation processes would finish the translation process near the top of the pyramid. This speeds up the process and makes it more efficient in translating lots of data.

For complicated translations, such as translating a sentence from Chinese into Arabic, two dissimilar languages that don’t share a common alphabet, the translation would use a lot of progressively complex processes, represented by the wider tiers at the bottom of the pyramid.

The translation process then transfers the rough translation to a series of direct translation processes. These processes clean up the grammar and syntax to be the same as the source language, and the translation process is finished.

The Future of Machine Translation

With the continuation of advances taking place in machine translation, it will one day be able to translate very dissimilar languages in a split second. You will someday be able to converse almost in real-time with someone half a world away speaking a foreign language like you were sitting across a table from them.

From the humble stutter steps that machine translation went through during its early development, the concept has hit its stride with the help of AI.

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