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The definitive guide to Machine Translation

RW
by Robert White
22.06.2022

Machine translation involves the use of computers to translate a text from a certain language to another. Many translation services involve the use of machine translation technology to properly execute important tasks. Machine translation is a triumph of computers and digital devices over a task that was so difficult to execute manually. For a long time, computer scientists have tried to develop an algorithm to enable machine translation.

Machine translation

They have recently just succeeded and the result is the machine translation tool. The tool is relatively a new invention and had recently spread widely to different parts of the world. The technology is still roughly ten years old. It is powered by a combination of digital frameworks like computing power, artificial intelligence, and sophisticated natural language processing.

What is machine translation?

Machine translation is the process of using a computer or a digital device to automatically translate a given text from one language to  language without any human intervention.

Machine translation was attempted in the 1950s, however, due to the level of computing power needed to execute the tasks, early computers were unable to cope with the challenge.

Machine translation would not be possible until another 50 years. In the early parts of 2000, software, required hardware, and data were able to perform basic machine translation activities.

Categories of machine translation systems

When it comes to machine translation systems, there are usually three most common types of systems for machine translation. They are:

Rule-based machine translator (RBMT):

RBMT is one of the earliest forms of machine learning. It comes with various serious downsides like needing a considerable amount of human-assisted post-editing, the need to add new languages manually, and general low output quality. It is helpful in some basic applications where you can quickly look for the meaning of words.

Statistical machine translator (SMT):

Statistical machine translation works by creating the statistical model which uses the interrelationships that exist between sentences, phrases, and words in a given text. SMT will apply this model to the second language for the conversion of the elements into a new language. This means that SMT works by improving on the RBMT model but ends up having a lot of its problems.

Neural machine translator (NMT):

The model makes use of artificial intelligence which enables it to learn and understand languages by way of contextual learning. It, however, works constantly to improve its knowledge database in the same way the neural networks of our brains would work. NMT has more accuracy, accepts newly added languages easily, and acts a lot faster if it has been trained. A lot of MT algorithms are fast adopting Neural MT as the standard in the industry.

Hybrid machine translator (HMT):

Hybrid machine translation is a mix of SMT and RBMT. HMT works by using its translation memory which makes it produce a far more efficient result. But then again, HMT comes with its massive disadvantages. The biggest downside of HMT is the fact that it needs a lot of human-assisted post-editing.

The advantages of machine translation

A translation agency will usually depend on machine translation due to their benefits. Some of the most significant benefits of machine learning are:

A fast rate of translation:

Machine translation can be used to translate a lot of words when it comes to massive translation projects. MT is also known to learn and become smarter. Machine translation makes use of AI to become smarter when more content gets translated. Also, machine translation is compatible with a TMS for managing and tagging content of high volume. This way, you are easily organised if you are looking to translate specific content into various languages.

An excellent selection of languages:

A typical machine translation system can translate multiple (50 to 100) languages. This means that they are written with programs with strong computational abilities to be able to translate several languages easily and simultaneously. This way, companies can launch their global products at once and update all documents accordingly. MT works best for language pairs such as German to French, Dutch to Spanish, Finnish to Flemish, Danish to Swedish, and so on.

Reduced Costs:

Machine translation is known to reduce the cost of translating written content. Despite when human translators are required to perform post-editing, machine translation is needed to minimise the total transaction delivery time as well as the costs involved. Machine translation is used to handle the initial heavy translation involved by providing basic and useful translations that can then be passed to a human for refining and editing. This way, any finished translation will lean closer to the original intent of the text, while the content will be localized effectively.

Machine translation: Things to consider

If you are making a decision on what MT system you should use, you will have to keep the following in mind:

Available budget: Neural MT when compared to Statistical MT, is more costly to train. However, the differences in cost can be justified by the fact that Neural MT produces quality improvement.

The required industry: Some business sectors usually have more technical and complex languages that would need a more sophisticated machine translation processing system. Neural MT has this feature.

The required language pairs: You can use Statistical MT for particular language pairs, especially with Latin-based language translation whose pairs have identical syntax and grammatical rules.

Quantity of available data: The Neural MT system will usually need to process a high quantity of text so that it will be able to learn. This way, you can easily leverage its benefits.

Customer-focused or in-house content: Customer-focused content, like marketing or sales usually reflect brand identity and quality. This means that they need the most innovative collaboration between machine translation systems and experienced human translators who will perform various post-editing tasks. In the case of basic internal employee documentation or communication, a basic machine translation system can be used to perform the translation especially when cost and timely delivery are important factors to consider.

Available machine translation engines present in the marketplace

When it comes to special engines for machine translation, there are quite a few that we can use. They are:

  • Google translate
Machine translation
  • Amazon translate
Amazon translation
  • Microsoft translator
Microsoft
  • DeepL
Deepl

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