Artificial Intelligence And Its Impact On The Career In Language Translation

Author: Abhinav Rai

Technology has brought about an era of unprecedented connectivity. However, across all forms of online media and platforms, the language connectivity is only hindered by language barriers. Even though technological devices make it easy to connect, often people from different linguistic backgrounds find it hard to connect and work with one another. However, artificial intelligence is now bridging the gap between technology and lack of understanding through intelligent translation solutions.

The earliest instance of using artificial intelligence for language translation was in 1954 when the Georgetown-IBM experiment used an IBM 701 to translate sentences from the Romanized Russian into English successfully. Since then, translation through artificial intelligence has taken a more sophisticated turn through the use of sentiment analysis and natural language processing. For instance, when Google incorporated neural networks to its translation tool, they made a massive improvement in rendering translation services through AI-powered translation systems that not only provide translations through text but speech as well.

The increasing role of AI in translation makes it a good option for language experts to enrol in artificial intelligence courses. Here is how artificial intelligence is impacting careers that are based around language translation:

Translation Through Neural Networks:

Neural networks, a form of artificial intelligence, is now being used to perform more accurate translations. Instead of translating bits and pieces of a paragraph, they translate a bunch of sentences at a time. The neural networks begin the translation with a process called encoding, which assigns numeric codes to individual words in the sentence. These numbers are then corroborated with the corresponding words with the same meaning in the language that is being translated into. Thus, by using machine learning models based on historical language data, neural networks deliver accurate translations.

Performing language translations through neural networks also give developers the advantage that they don’t need to create extensive algorithms and rules like they have to do for other alternative systems. Through machine learning, the neural networks teach themselves by studying several examples based on different kinds of translations. Further advancements in the neural network technique are that instead of learning from various models, the network systems would be linked to the entire language database, thus leaving AI to conclude on its own. These systems offer a perfect career option for those who have a knack for artificial intelligence and have a distinct interest in how linguistics work.

Ai As Assistance For Human Translators:

AI is also being used readily as a tool to assist human translators. These systems make use of augmented intelligence to show human translators suggestions for appropriate nouns and word alternatives, and also help them to edit translated content. This AI-powered technology works on a recognition system that analyses standard language words that are being translated and then checks them for accuracy, fluency, and grammar. These checks are made against native texts in the targeted language, and then suggestions are formed on the instances of discrepancy detected in the translated content. So, for language translators, getting an understanding of how these translation system works can help them deliver better-translated content in shorter periods of time.

Statistical Translation Methods:

AI is also frequently used through statistical models to perform the translation. These mathematical models compare bilingual systems to compare texts for corresponding elements which hold similar meaning. These models are best used for translating formal documents that make use of standardised language which does not have much in common with everyday speech.

For using translations on informal text, artificial intelligence is employed to mine information from colloquial speech like social media posts and other unstructured content to accurately communicate more sophisticated elements like idiomatic expressions, figures of speech, and other features that are used in informal writing. An understanding of this logical process of translations will give translators an edge in rendering translations whose meanings are as near to the intent of the original content as possible.

Translations Through Bilingual Dictionaries:

Artificial intelligence has made it easier for language translators to perform complex translations on the go. By making use of a bilingual dictionary, AI-based models make etymological connections between words of similar meanings. This allows AI to make connections and clusters from one language to learn how the other language operates. For making accurate translations quickly, AI uses the dictionary for techniques like back translation and denoising.

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In back translation, every sentence is converted into the new language, and is translated back again using the bilingual dictionary, called reverse or ‘back translation’. If this reverse translation does not match the original text, the AI system reworks on its attempt to make a more accurate translation. Denoising works in a similar fashion, but instead of translating sentences to and fro between one language and the other, it keeps reworking on the sentence structure to make sure that the AI keeps learning to generate genuine translations.

Future Of Translation Is Ai:

When it comes to AI-based translation systems, the innovations are just beginning, and are going to stay for a long time. As technology makes the world smaller, there is an ever growing demand for smarter and faster translations. Artificial intelligence makes this task more accessible by providing translations that are in line with the intent of the content. Thus, investing in an artificial intelligence course will give an edge to language translators in their career.


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Abhinav Rai is the Data Analyst at UpGrad, an online education platform providing industry oriented programs such as data science, digital marketing, artificial intelligence and machine learning courses in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS etc.


Image credit to Alex Knight  | Image credit to rawpixel  | Image credit to rawpixel

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Abhinav Rai
Author: Abhinav Rai
Abhinav Rai is the Data Analyst at UpGrad, an online education platform providing industry oriented programs such as data science, digital marketing, artificial intelligence and machine learning courses in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS etc.