Machine Translation (MT) – Key success process in natural language processing. This tool helps to translate one language to another with high accuracy. This post will focus on high-level arguments around machine translation only to you can find out more details on Machine Learning Basics here.
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. It sounds simple, but on the surface floor, it is far more complex.
A translator interprets and analyses 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 techniques, semantics, etc. The biggest role is played by locals who are familiar with the geographical regions.
Into the Limelight
It’s difficult to ignore GT- Google Translate; when talks are about language translation. GT is in presence for decades. Sadly apart from the development and technology enhancement, GT still has too many challenges.
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 correct and slow. What will you do?
No worries..!! The advancement is AI service brings the various products which are developed to give correct solutions to users. A content intelligence solution provider named Abbyy has improved the TextGrabber application supported to iOS devices with significant updates, which has emerged as a powerful alternative to Google Translator.
Some More Insights
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.
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 color or any kind of background. You don’t need to download any kind of language package to translate your image test in offline mode.
How does it work?
The 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 carry out 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 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 the Artificial Intelligence Course which covers critical topics like ML, deep learning with TensorFlow, etc.
Rule-Based Machine Translation (RBMT) model follows the same approach as a language that is based on a bunch of grammatical and syntactical rules. To get a correct translation of a phrase, the application requires a linguistic dictionary for both 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 by 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-intensive and unlike RBMT it can be applied to multiple languages. Getting more attention from the developer’s community and professionals is obvious.
Final Words to Take Home
With such incredible features and services, the MT technology market is expected to reach 983.3 million USD by the end of 2022. The growing market of cloud computing has to 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.
Points to Note:
All credits if any remains on the original contributor only. The guest author has covered all basics around Machine Translation only Learning. Machine Learning is all about data, computing power and algorithms to look for information. How machine can do more than just translation this is covered in Generative Adversarial Networks. A family of artificial neural networks.
Feedback & Further Question
Do you have any questions about Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.
======================== This is a Guest Post =================================Danish Wadhwa – Governs the digital content to assemble good relationships for enterprises and individuals. Danish is a SME in digital marketing, cloud computing, web designing and offers other valuable IT services for organizations. His efforts eventually enhance companies shape by delivering the stupendous solutions to their business problems.
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