Neural Machine Translation: Modern AI technologies create almost unlimited multilingual communication

Nowadays, the way we live is being changed by Artificial Intelligence (AI) and one of the aspects of AI that is not that often mentioned but that still has us excited is that it is changing the way we communicate, both within our own language and internationally. Can you imagine that you could travel the world or participate in business negotiations with other people halfway around the world, without sharing a common language? Can you imagine you could be able to talk to and understand perfectly everyone in real time no matter the language, just by putting some headphones in, for example? Well, the technology is here to make that possible through Neural Machine Translation. This article aims to inform about the latest trends in machine translation and how they have changed the way people communicate. After reading the text, one will have a better understanding of the application of Neural Machine Translation in different industries and how AI-powered tools might change business and society as we know it.

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Communication in the digital age: The impact of Artificial Intelligence on today’s communication

Communication is without any doubt an essential part of the human experience. And being such an important aspect of life, it is only natural that one expands upon it, making it more comfortable, more efficient, and giving everyone the ability to communicate with everyone else, no matter the place and distance. In fact, technology and its improvements have long been a driving factor behind making communication much easier and far-reaching, with prominent examples being fax machines and the telephone. However, newer trends in technology are making even bigger changes in the way people can communicate with each other. AI and related technologies have found their way into almost every industry, with benefits too valuable to be ignored. And here comes the question: How important is the role that AI has played in human interaction?

 

Well, to answer this question, you might ask yourself when was the last time you wrote a handwritten letter on paper, mailed it across the country, and waited for an answer letter back? Most probably a long time ago. Once upon a time, people had limited options for exchanging information with one another. But today, older forms of communication such as writing letters are quickly becoming outdated because of the huge improvements AI has brought to communicating with others. This connectivity has made communication faster, easier, and more convenient, prompting every generation to become more capable of mastering these new technologies to keep up with new forms of communication. The older generation is enjoying the ease in which they can send e-mails instead of writing and mailing the good old paper letters. At the same time, the newer generations are propelling these technologies even further as they are simply born into this tech era, and are therefore very tech-oriented by nature. Young people can now talk, send photos or videos to their grandparents and other family members who happen to live far away, and vice versa. Today’s communication is instant, allowing for more frequent connections — not just between family, but for all humans and it only requires that you and the others have an internet connection. So simple. Now we have the chance to communicate so easily with each other. However, while we have the chance now to talk to someone who lives in the other part of the world whenever we want, there are still some other aspects that may stand between people and the barrier-free global communication – the language.

 

Nowadays, globalization is a fact. The world is coming together and this has presented a communication problem. There are both many personal and professional situations of language barriers and having readily available translations would change the way people experience one-on-one communication. If people could understand everything and everyone everywhere, this would be something like the entire world using the same linguistic currency. What can make that possible is Neural Machine Translation.

Based on neural network technology, Neural Machine Translation (NMT) is one of the top-rated approaches to artificial intelligence and machine learning. The approach offers near-perfect human translation to morphologically rich languages too. When used the right way, Neural Machine Translation is capable of some amazing results.

 

Removing communication barriers with AI-powered technology: Neural Machine Translation at its best

The dream of being able to automatically translate from one language to another has come true with the invention of the Neural Machine Translation. Neural Machine Translation or NMT for short, is a unique approach to machine translation that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It basically is a technique that is used for translating one language to another, for example, English to Spanish.

Let’s imagine you are currently in a small Spanish village where people do not understand English. However, you still intend to communicate effortlessly with the local people there. In such a case scenario you can make use of Neural Machine Translation.

What makes Neural Machine Translation so amazing is that it translates entire sentences at a time, rather than just piece by piece. It uses the context of the sentence to figure out the most relevant and correct translation which then rearranges and adjusts until it gets as close as possible to an actual human speaking with proper grammar. In fact, it is like people translate what other people say. But how is all that possible?

 

Making sense of NMT: How exactly does Neural Machine Translation work?

Neural Machine Translation systems are made up of artificial neurons, connected to each other and organized in layers. They are inspired by biological neural networks, capable of learning on their own from the data they receive each time someone translates a text.

The actual “learning” process consists of modifying the weight of the artificial neurons. It is repeated during every new translation to optimize it and thus the quality of the following translations. Neural Machine Translation systems work with bilingual corpuses of source and target documents that have been translated in the past. The translation itself works in three phases.

The first one is the analysis phase when the words of the source document are encoded as a sequence of vectors that represent the meaning of the words. The context is generated for every single word, based on the relation between the word and the context of the previous word. Then, using this new context, the correct translation for the word is automatically selected among all the possible translations this particular word could have.

When the analysis is finished, the sentence is transferred by a second process, called “decoding”, into a representation of equal or slightly less depth in the target language. Then, a third process named “generation” generates the actual target sentence from this internal representation, i.e. a meaningful sequence of words in the target language.

 

Back to the birth of NMT: From machine translation to Neural Machine Translation

Machine translation, shortly MT, is the ability of machines to perform automatic translation of text from one natural language to another. The life-changing idea of doing translation using machines was first introduced in 1949 by Warren Weaver. Until 1980, machine translation was done through the study of linguistic information about the source and target languages, generating translations based on dictionaries and grammars, which is known as rule-based machine translation (RBMT). With the development of statistics, statistical models started to be applied to machine translation, which generated translations based on the analysis of bilingual text corpus. This method is now known as Statistical Machine Translation (SMT). SMT, in fact, gained better performance compared to RBMT and therefore dominated the field from the 1980 to 2000s. Just a few years later, in 2003, a group of researchers from the University of Montreal came up with the idea and developed a language model based on neural networks which improved the data sparsity problem of traditional SMT models. Their work laid the foundation for the future usage of neural networks on machine translation.

Only ten years after, in 2013, the researcher scientists Nal Kalchbrenner and Phil Blunsom proposed the idea of a new end-to-end encoder-decoder structure for the already known machine translation. The model was supposed to encode a given source text into a continuous vector using Convolutional Neural Network (CNN), and then use Recurrent Neural Network (RNN) as the decoder to transform the state vector into the target language. And this is the moment that can be called the birth of the Neural Machine Translation (NMT), thanks to the work of Kalchbrenner and Blunsom.

 

Neural Machine Translation: How the latest technology improvement can help your business

Language barriers affect businesses of all kinds. As the world becomes smaller thanks to the technology improvements, businesses encounter difficulty in accommodating the needs of an increasingly international consumer base. Hiring translators can be expensive, which is why utilizing technology to perform translation services is a cost-effective option for increasing understanding and promoting inclusivity.

And this is where Neural Machine Translation comes into play. Neural Machine Translation is a major breakthrough in language technology that some believe is a turning point in how business gets done. It is that thing that can give businesses (especially small businesses with limited funds and resources) access to high-quality translation services that are efficient and cost-effective. For example, Neural Machine Translation could help produce menus, user manuals, and warning labels as well as translate large documents in several languages quickly and more affordably than human translators or in the traditional machine translation process, which still leave a lot to be desired in terms of quality.

From communicating with customers and investors who speak other languages to translating contracts and legal documents, Neural Machine Translation can help businesses in many different industries keep up with the pace of the global economy.

 

Neural Machine Translation application in the travel industry

Speaking about businesses and areas where fast and efficient translation is essential, we cannot miss the fact that the travel industry, as it is one of the global industries in which multilingualism is a fact of life. Maybe travel agents want to provide contents of various interesting destinations for their outbound travelers or want to reach out to potential inbound travelers in different countries to invite them for a very special and limited offer. Or maybe for booking an apartment or a hotel room, a guided group trip for the weekend, event, etc. – the core for all of this is proper communication, and related to communication is localization. Without an effective and efficient translation, the system communication level will not be satisfying. But with the help of a translation system that involves a neural network, it is possible to deliver an effective experience to customers on a global level.

Nowadays, almost everything happens online. And for travel service providers, an online presence is essential. It is the main advertising space, as well as a touchpoint for sales and customer service. Managing all the online content, including all the constant updates, and making them accessible in the target market’s language in near real time is imperative. The conventional approach of localizing would be a human-only translation which is often time-consuming and very pricey. However, in a fast-paced sector as the travel industry, time is highly important, and localizing content in a timely manner to make them available as soon as possible is essential in order to stay competitive.

While machine-based solutions can assist the human localization process, for some use cases machine translation technology can be preferable. For example, live chat solutions for providing multilingual customer support as well as sales, be they bot or human-supported, to support global customers can be enabled by real-time machine translation. And since communication really is at the core of the travel sector, communicating over different channels with every customer and potential customer is of huge importance for each provider. That being said, not only live chat, but customer support over the phone can also be done with the help of Neural Machine Translation, because it would be very costly to recruit an agent for every language covered when it comes to customer requests and complaints. It is nevertheless crucial to respond to customers in their native language quickly, efficiently and at minimal cost without affecting the quality of the service itself.

Thanks to Neural Machine Translation, more and more languages are being added to databases. And for travel agencies that operate in different parts of the world as well as their customer support teams, the response time in foreign languages can be drastically decreased after Neural Machine Translation implementation. As a result, calls are reduced with increased usage of a multilingual online knowledge base, and customer satisfaction is higher. There is still room for improvement, but Neural Machine Translation has taken major strides in bridging the gap between people, languages, and destinations. And the travel industry stands to get many of the benefits.

 

The new era in Education: Neural Machine Translation in foreign-language education

When it comes to training and education, effective and relevant communication unlocks the potential of every individual. Neural Machine Translation can be of great use to personalize information and communication via any e-Learning platform. The system could facilitate conversation without any barrier. Moreover, new AI translation tools are great for both self-study and use for classroom aids. They can be very helpful in foreign-language learning. Both students and foreign-language teachers can benefit from AI-powered tools, which should make their efforts easier. Applying AI in foreign-language education provides learners with immediate and highly individualized support, which is a fundamental building stone for personalized learning as one of the ideal standards of contemporary pedagogy. In this aspect, Neural Machine Translations and other AI-powered tools are a bit ahead of real teachers who simply cannot continually analyze every student´s outputs, adapt to everyone’s individual learning needs, and give students well-grounded feedback within seconds. And this is all in a class of for instance twenty students. AI-powered tools are, on the other hand, able to collect massive amounts of data on learners’ learning progress, to model their learning curves on that basis, and to adapt learning content accordingly. Additionally, they enhance students´ progress through the functionality of small consequential steps and immediate feedback. This is the reason why different machine translation tools can be used by teachers as very effective support because they can free teachers from tiring, energy- and time-consuming activities integrated into the learning process of every language, such as grammar or pronunciation exercises.

 

Artificial Intelligence takeover: humans vs. machines

To keep up with today’s competitive world, one has to keep up with Artificial Intelligence. What is more, artificial intelligence is being integrated into everyday life. The technology is so advanced that, in some cases, you may not even realize it is there. This may scare you or may intrigue you, but in any way, Artificial Intelligence is more life-like than ever before – from robots at your local airport to security systems at your favorite beauty salon. Nowadays, the topic of the Artificial Intelligence revolution is very debatable. In fact, everybody has an opinion about Artificial Intelligence but at the end of the day, it is always about mixed feelings. Artificial Intelligence has been put into question time and time again. The future possibilities of this technology are often compared to the Pandora’s Box myth. People keep asking whether Artificial Intelligence will soon be completely able to replace human beings?

Of course, it is true that AI has some amazing applications in different fields of human life. But as the applications of AI increase, it is quite natural that people are starting to wonder if it is going to erase the importance of human skills and experiences in different tasks.

Mentioned above are examples of how AI can be applied in a wide range of industries. But the question is: can it be used in all of them? Even though AI is helpful, improves productivity, and is cost-efficient compared to human beings for multiple fields of studies and works, there are still other areas where Artificial Intelligence is not exactly that effective. Examples can be a human resource manager of a certain organization or an artist. There are still many fields that require a greater amount of human interaction, sympathy, experience, as well as skills. Certain positions, including writing and editing, graphic designing, and others are jobs that still need the human touch. Of course, with the help of AI-based systems, everything will be a lot easier. But AI cannot completely take over tall of the tasks, at least not yet. Academic fields, too, are exempt from the AI automation trends. Tasks such as grading papers can be assigned to the AI system, but when it comes to researching, for example, the field still requires human influence and presence. The key thing to keep in mind is that despite all of the capabilities of artificial intelligence in general, people are the ones that are still in control. And whilst artificial intelligence helps to mitigate our weaknesses and supplement our work, the technology also mirrors the quality of the human input.

Artificial intelligence
Neural Machine Translation
technology
machine translation