Artificial neural networks: the future of machine translation
The concept of machine translation dates back decades. At first, it was just a rule-based machine translation system. Then in the 80s researchers developed statistical machines translation. But in the twenty-first century, the leap in machine translation technology will come from machine learning and neural network technology.
In the past few years, we have seen major developments in a number of translation companies that are using artificial intelligence technologies to provide better machine translation. The most impressive examples are coming from Google’s Neuromechanical Translation System (GNMT) and Microsoft’s Nerve Translator.
- What is a neural network machine translation?
Machine translation has been used for many years and you can find machine translation systems in many popular applications. However, there is a significant difference between existing machine translation services and the developing of neural networks translation systems.
Most systems that you can now discover are statistical machines translation, they use algorithms and statistical models to translate sentences. On the contrary, neural networks translation represents a completely different approach and uses deep learning in order to analyze a large amount of human translation. By analyzing this vast data set, the system can interpret the entire sentence, understand the context and the different variations and ultimately to make the translation more fluid and natural.
- How does a neural network translation work?
The key to provide this learning ability is neural networks. The idea is to mimic the interrelationship of neural pathways in the brain. With this technique, computers can learn, recognize, and make decisions that resemble to the human brain. Artificial neural networks replicate the human brain structure. There are tends of millions of artificial neurons arranged in layers. On the one hand, the input unit is designed to receive information and on the other hand the output unit indicates the response to the message.
Compared to the statistical system, the structure of artificial neural networks enables the system to translate more and more complex models. Moreover, the system can also learn from experience: if it provides incorrect output, it can learn from mistakes and make adjustments to perform tasks more efficiently next time.
Neural network translation is still a new technique, but even in its early stages it provides a better result than statistical machines translation.
With the development of technology, neural networks translation will become an important tool and as translation is going to be more complex, people will use these systems to get more accurate translations in less time.