Rule-Based vs. Statistical vs. Neural Machine Translation

Last Updated November 18, 2019

Machine translations based on rules and statistics have long been considered as clumsy and unnatural. After all, language is not only words, but also small nuances and rules that are difficult to keep in check.

To translate text, it’s not enough to swap phrases from A to B. It is crucial to understand grammar and be aware of language and cultural habits. Artificial intelligence algorithms and neural networks are becoming more and more proficient in that field, creating almost perfect translations.

Let us check how these technologies work.

If you primarily associate machine translation with the beginnings of Google Translate, you may be surprised to learn that there are three types of computer-aided translation systems, namely:

  1. rule-based machine translation
  2. statistical machine translation
  3. neural machine translations.

The latter is the most advanced and meets strict requirements of the translation industry.

New technologies and requirements drive change

Translation has to be fast, error-free, and consistent, especially when e-commerce or technical industries are concerned.

In those branches, hundreds of specialists are simultaneously working on client inquiries, manuals, or product descriptions.

That’s why modern translation systems are based on fast and efficient deep learning engines. The same technology drives the development of object recognition which is used in monitoring systems or even in passenger cars.

Deep Learning vs. Machine Learning – the Devil’s in the Details

In machine learning, the computer derives knowledge from a human-supervised process. Thousands of training examples are introduced into the machine, for example photos of cats and elements specific to these animals. During the next phase, man corrects errors made by a program. In this way, the system learns to recognize a cat’s image from thousands of photos. However, such a learning process is quite time-consuming and has some limitations.

Deep learning is a subcategory of machine learning technologies. Deep learning is a breakthrough because it does not have to be supervised by humans. It involves creating large neural networks allowing the system to learn and operate independently. The linear logic peculiar to computer programs is replaced by a method modelled on the operation of the human brain. The software learns and perfects after each new experience.

Machine translations based on the work of the human brain

This is how neural networks used in modern machine translations work.

Machine translation specialists have developed systems that are widely used in business today. They can be fully integrated with CMS systems and process automation tools, which makes translations and content implementing almost a real-time activity.

The system stores the data used, so that they can be reused in the future translations. As a result, the whole process is being constantly improved because the machine learns on its own mistakes.

machine translation

It is extremely convenient for global multilingual enterprises. Although it is more and more often said that artificial intelligence can replace a human being, even in the case of such advanced technology, specialists have to deal with post-editing and verification of machine translations.

Rule-based and statistical-based systems

Translations based on neural networks perceive the task as a whole, also taking into account the context. Therefore, such translations are much more natural than those based on rules (RBMTs). This older technology is a combination of dictionaries and language and grammar rules. In order to analyze a text, the system requires extensive lexicons and a complete set of language rules.

Statistical systems are more advanced. They do not use rules but “learn” by translating large amounts of data. Therefore, they work well, for example, for translations for a specific industry. However, the method is not always accurate since, for instance, word order differs depending on the language. It is not surprising that this is not a perfect technology – it is almost 70 years old.

Related Posts

Summa Linguae uses cookies to allow us to better understand how the site is used. By continuing to use this site, you consent to this policy.

Learn More