Can Technology Replace Human Interpreters?The Future of Translation Technology

translation industry

Last year, the world witnessed a drastic change in shaping the translation technology. Uncovering the two major Artificial Intelligence systems, which are capable of learning any languages known to humans.

Most of the major companies are already experimenting to make the translation technology better for the online consumers.

Last year, the big e-commerce giant Amazon joined the group by launching Amazon Web Series. The Translate app provides translation for all the languages supported to promote the products and services with ease.

The big corporate giants like Google and Microsoft offering the translation technology for a long time, individually.

At this point do we really need another translation application? Is the technology is improving? Or just the diversifying the option where to get it from?

Translation Technology

One of the In-Demand Services Of 2017 – Translation

According to Global Market research, the translation industry will hit 1.5 billion dollars by the end of 2024.  So, this considered as the good news for translators and also for those who are closely working with translation application.

In a study conducted by the Bureau of Labor Statistics highlights, around 17% of the employment growth for interpreters and translators 2026. As this is becoming the strong need for business organizations to promote business globally.

The growth is considerably faster than other that of other occupations. In light of the fact, companies are also offering more than 10K position on this fast track.

Amazon’s AWS backs up this act of pushing the business growth globally. Business entities are focusing on expanding globally by merging the translation tools and software into a business. However, to reach out to a significant customer database overseas, they need to offer more localized language. Which is beyond expectation at this point for any translation tool.

New Opportunities For Translators

Technology is opening up some new branches for translators. Chinese, German, Russian, Spain, Portugies are some of the major languages impacting the global market. Among others, these languages open a bigger job perspective for the translators.  

On the other hand, it is not unusual to think the translation tools are cutting the edge for human translators. For a fact, they are making the job opportunity.

In spite of technical advancement in the field of Artificial Intelligence, and machine translation, it still needs close human supervision. Close supervision by the professional translators can ensure correct dialect and use of grammar.

Even though machines are using the improved and advanced algorithms, still when it comes to a faceoff, it still can’t beat the human translators.

Sejong Cyber University put three machine translator against a group of human translator into a test. Results appear to be quite disappointing for machines. They had to struggle to live up to the expectations for the creator. Machines are, of course, faster than human translators but they are likely to make mistakes while completing the sentences.

During the building of the machine, it requires a specialist knowledge to minimize this problem. To make this technology accurate, hiring a technical translator should deliver a better end product. With a precise technique, business entities can reach to global customers

Industry Specific Translation Apps Are Coming Out

Machines are not yet entirely accurate when it comes to translating a piece of document and information. They are still making grammatically incorrect sentences, making it less reliable than a human translator. For industry-specific machine translators, it has to be more accurate. For instance, law, medical, education, which is far beyond the capacities of human translators.

To provide accurate information on this type of translation, interpreters should be trained under terminology. While the developers work on the advancement of the technology, to make the technology accurate the human translators will look forward to the global expansion of the business.

Artificial Intelligence Can Translate Languages Without Human Interference

Artificial Intelligence

The era of Automatic translation of language has come a long way. This is all because of Neural networks computer algorithms that recognize the inventiveness of the human mind.

However, there is a huge requirement of data to train such networks. Millions of translations from sentence to sentence are required to show how a human would usually do it.

Today, there are two newspapers that show how neural networks can learn to translate without two parallel texts. This astonishing advanced step will ensure that the documents are widely accessible and in several languages.

Artificial Intelligence

Artificial Intelligence Goes Bilingual

Mikel Artetxe, a computer engineer and a scientist at the University of Basque Country revealed that they have a computer which can translate two languages.

Mikel presented his invention by giving an example of a situation when Chinese and Arabic books are given to learn and consequently to translate them. This seems like an impossible task for a normal human being. However, this is now possible. Thanks to Mikel who shows that it is possible for a computer.

A lot of the machine learning works under supervision. That is, say a computer makes a guess and receives the right answer. Then, it instantly adjusts its process accordingly. This method works smoothly when a human teaches the computer to translate popular languages like English and French. As a lot of documents are available in both the languages.

These two new papers focus on another method, that is Unsupervised machine learning. In this method, each of the computers is given out bilingual dictionaries without the help of a human tutor to tell them which of their guesses are correct.

This process is a possibility because in languages there is a lot of similarity between the words that gather around each other. As an example, words like Chair and Table are together in every parallel language.

Methods Used By The New Papers

The new papers use methods which are very similar. They translate on a sentence level. They use a couple of training methods. One is back translation and another is denoising.

In the Back translation, one sentence of a language is barely translated into another language and its translated back to the original language again. If the back-translated sentences are not similar to the original language then the adjustments of the neural networks should be in such a way that from the next time the translation is closer.

Denoising method is kind of similar to Back translation. However, it works in a slightly different manner. Instead of translating from one language to another and vice versa, Denoising adds more noise to a sentence. Adding noise means removing and rearranging the words and translating it back to the original.

These methods together provide more knowledge about the deeper structure of language to the network. There is a little difference between the use of the techniques to translate languages.

The UPV system back translates constantly during the training period. Whereas the other system created by a Facebook computer scientist, Guillaume Lample adds an extra step to the translation process.

Although both the systems encode a language into an abstract language before decoding it back to the original, the Facebook system does it with a slight difference. This system makes sure that the other language is truly abstract or not before the encoding and decoding process.

Artificial Intelligence Rising To Incredible Heights

In addition to the translation of language without many parallel texts, the system methods can help with the translation of common pairing languages like English and French. This could help if the parallel texts are of the same kind, as the newspaper reports, but you want the translation in a new domain such as street slang.

It came as a shock for Di He, a computer scientist in Microsoft, that the computers could learn to translation without the help of a human. Although he also added that this shows that the approach is in the taking place in the right direction.