Neural Networks are the Future of Machine Translation

The article is written and submitted by Rachel Wheeler. 

The idea of machine translation stretches back many decades. First, it was just a system for rule-based machine translations. Then in the 1980s, researchers developed statistical machine translations. In the 21st Century, the great leap in machine translation technology is going to come from machine learning and neural network technology. 

Over the last few years, we have seen some significant developments from translation firms that are using these Artificial Intelligence technologies to provide better machine translations. Some of the most impressive examples have come from systems like the Google Neural Machine Translation (GNMT) and Microsoft’s Neural Translator.

What is a Neural Machine Translation?

Machine translations are already widely available, and they have been for years. You can find machine translation systems in a number of popular apps, and there are online services that offer machine translation services. However, there are significant differences between the services that are already available and the systems that are being developed to use neural networks.

Most of the systems that you would find right now are statistical machine translations. They use algorithms and statistical models to make a best guess as to the proper translation of a phrase. It can work well for short phrases, but it reaches its limits when it is asked to translate longer samples, like an entire digital textbook for an elearning translation. The results could be anything from a pretty accurate interpretation to a translation that is complete gibberish.

Neural machine translations represent a very different approach from the older systems that use statistical machine translations. With an NMT system, a neural network is trained and optimized to perform translation services.

These systems use deep learning to analyze vast amounts of translations that have already been performed by human translators. By analyzing this large dataset, it can account for whole sentences, understand context and different variations, and work with linguistic subtleties that could not be programmed into a statistical based model. The end result is a machine translation system that is more fluent and natural.

How Neural Networks Work?

The key to providing machines with this ability to learn is the neural network. This is a type of computing technology that is designed to mimic the workings of the human brain. In the brain, neural pathways are formed as we learn. If the pathway is useful and beneficial, it gets stronger. If it does not provide the correct results, it gets weaker.

The idea behind neural networks is to simulate the interconnected nature of the neural pathways in the brain. With this design, computers can learn, recognize patterns and make decisions in a way that is similar to the human brain.

Artificial neural networks simulate this structure by having anywhere from dozens to millions of artificial neurons known as units. These units are arranged in layers. On one side, you have input units that are designed to receive information. On the opposite side, you have output units that signal the response to the learned information.

Between the layers of input units and output units you have the hidden units. These are the layers that comprise most of the neural network and they are also used to form most of the connections.

Each connection in a neural network is given a weight to indicate the strength of the connection between the two units. The weight can be positive for a connection that excites the other unit, or it can be a negative weight for a connection that suppresses the action of the other unit. The higher the weight, the greater the influence the one unit has over the other in the connection.

Neural networks learn by comparing the output produced to the desired output. Information is fed to the system at the input end. It then triggers connections in hidden units. This leads to a connection with the output end. The output that network actually produces is then compared to the desired output. The weights are then modified based on the difference between the produced output and the desired output.

For machine translations, the structure of a neural network makes system more adaptive and able to handle more complex models than a system based on rules and statistics. It can also learn from its experience. If it does not provide the right output, it learns from the mistake and makes adjustments to perform more efficiently next time. 

What Does It Means for Machine Translations?

Future of Machine Translation

Neural machine translation is still a new technology. Even though it is in its early stages, it already provides results that are superior to the best statistical machine translation systems. That said, we have a long way to go before neural machine translations could compete with a skilled human translator.

Read: Artificial Intelligence vs Machine Learning vs Deep Learning

The advancement of neural machine translation is not going to come from one company. It is going to be the result of several advancements from researchers at different organizations and around the world. As the technology develops, NMT is going to become an important tool for human translators. With the need for complex translations growing, professionals will use these systems to provide accurate translations in less time and this will help them to meet the growing demand.

Written by
Varun Kumar

I am a professional technology and business research analyst with more than a decade of experience in the field. My main areas of expertise include software technologies, business strategies, competitive analysis, and staying up-to-date with market trends.

I hold a Master's degree in computer science from GGSIPU University. If you'd like to learn more about my latest projects and insights, please don't hesitate to reach out to me via email at [email protected].

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