Artificial Intelligence

Quick and Accurate Machine Translation Models

Machine Translation (MT) – Key success process in natural language processing. This tool helps to translate one language to another with high accuracy.

 

What is Machine Translation?

Machine translation (MT) is an automated translation process used by a computer application to translate a natural language text into another. Such as translation of English into Spanish. In the translation process, the meaning of the source text must be already stored in the destination i.e., target language. Sounds simple, but on the surface floor, it is far complex.

A translator interprets and analyse all the keywords or symbols in the text. It also understands how each word affects another. For creating such a complex system, it requires expertise in grammar, sentence structure, coding, AI, semantics etc. Also need of locals who are familiar in geographical region.

 

Into the Limelight:

It’s difficult to ignore GT- Google Translate; when talks are about language translation. Though GT is in presence from decades; sadly apart from the development and technology enhancement, GT still has too many challenges.

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One of the critical issues was- what will happen if you moved to any remote area or any unfamiliar country with no internet connection? Also, you forgot to download their native language before. As you know, the image-to-text based translation models are not accurate and slow. What will you do?

No worries..!! The advancement is AI service brings the various products which are developed to provide accurate solutions to users. A content intelligence solution provider named Abbyy has improved TextGrabber application supported to iOS devices with significant updates, which has emerged as a powerful alternative to Google Translator.

The application is marked with the power of real-time translation function. It uses a smartphone camera to capture and translate the text immediately. And the best part is- it works online and offline too. It can translate the text of any colour or any kind of background. You don’t need to download any kind of language package to translate your image test in offline mode.

In May 2018, a social media giant Facebook added 24 new languages to its platform for improving customer interaction. Facebook has leveraged artificial intelligence to enhance its neural machine translation models. These translation pairs are- Serbian and Belarusian to English in Europe and other countries. In a report, Facebook revealed that more than six billion translations are performed on its platform every day. In the same month, the company open-sourced its neural machine translation model- PyTorch 1.0 and some of its AI tools for developers and AI experts.

 

How does it work?

Machine translation model renders text from one natural language to another. There are various MT models developed to effectively drive translation based application souls. To accomplish this task, experts rely on the powerful approaches used to build these models. It can be Rule-Based, Statistical, Neural, Hybrid or Example-Based MT.

The field is very vast and it is not possible to cover the whole model in a single article. So, I’m only gonna cover Rule-Based and Statistical ML approaches. To cover up the basics to advanced areas of artificial intelligence and its associated technologies you can join Artificial Intelligence Course which covers the critical topics like ML, deep learning with TensorFlow etc.

Rule-Based Machine Translation (RBMT) model follows the same approach as a language which is based on the bunch of grammatical and syntactical rules. To get an accurate translation of a phrase, the application requires a linguistic dictionary for both the languages. It should include a proper set of rules for sentence formation structure for both the languages. RBMT is most popular among professionals because it can give a better quality of language pairs with multiple word orders.

Statistical Machine Translation (SMT) is developed on the concept of probabilities. For each chunk of the source phrase, there are various possible target chunks defines on the basis of the probability of which one is the correct translation. An application chooses the chunk with the highest statistical probability of being a correct translation. Since SMT is not developed on the basis of resource intension and unlike RBMT it can be applied to the multiple languages, so it is getting more attraction of developers and professionals.

 

Final Words to Take Home

With such incredible features and services, MT technology market is expected to reach 983.3 million USD by the end of 2022. The growing market of cloud computing has lead this technology to offer Machine Translation Software as a Service (MTSaaS) offered by various platforms. Thus, anyone can predict how this technology has emerged as an advanced tool hastening the future of human-made translation applications.

 

MachineLearning 

 

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Danish Wadhwa  – Governs the digital content to assemble good relationships for enterprises or individuals. He is specialized in digital marketing, cloud computing, web designing and offers other valuable IT services for organizations. His efforst eventually enhance their shape by delivering the stupendous solutions to their business problems.

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