Machine translation (MT) is the automatic translation of a text into another language using computer software. Human interaction is dispensed with. Various translation methods are used in machine translation.
The first technologies in the field of machine translation were developed in the USA in the 1960s. The military in particular hoped to gain a war advantage by translating foreign-language documents. However, research was put on hold and only resumed in the 1980s by German and Japanese scientists. Since 2006, machine translation has been available to the masses thanks to Google Translate. Artificial intelligence gave automated translation a huge boost in quality in 2016, but the technology is not yet perfect.
How machine translation works
The aim of a professional translation, whether done by man or machine, is to translate a text in such a way that the meaning corresponds to the original. Translators must therefore interpret and evaluate all text elements in order to produce a correct translation. There are many translation programs online that convert texts into another language within a matter of seconds. They use different methods for language translation, which we would like to introduce in the following paragraphs.
Rule-based machine translation
Rule-based machine translation (MT), used mainly in the 1980s, provides a literal translation. This is supplemented by language algorithms that combine the linguistic and grammatical rules of both languages. This rule-based translation system usually delivers consistent and logical translations, but is quite prone to errors.
Example-based machine translation
In example-based machine translation, the system makes use of a translation memory in which frequently used sentences or phrases are stored together with their translations. Instead of translating a sentence literally, sentences that are statistically most similar to the input sentence are retrieved and the translation is generated from them.
Statistical machine translation
Statistical machine translation uses machine learning instead of an algorithm to translate text into another language. Before the actual translation, the system is “fed” with a huge amount of data of texts of the source and target language and can derive a bilingual dictionary as well as grammatical peculiarities. The texts are translated on this basis. The statistical method is popular because it provides fluent, if less consistent, translations and requires no knowledge of the languages involved.
Neural machine translation
Neural machine translation is based on a similar principle, but here the system learns via a large neural network (similar to a brain) to grasp connections between the source and target language. As with the statistical approach, the machine relies on training material and, thanks to artificial intelligence, can predict the probability of a sequence of words. The system works so independently that the exact processes are not known. Neural translation is becoming increasingly popular because precise translations are possible in real time.
Machine translation vs. human translation
A major advantage of machine translation is speed. In this way, texts can be translated in seconds that would have taken a professional translator several hours. However, online translation programs are unsuitable for certain translations and this is particularly true for the marketing and financial sectors. However, texts in the legal or medical field as well as websites should also be prepared by a professional translator with a high level of quality and cultural knowledge. Even certified translations have to be done by a human being.
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FAQ: More questions about machine translation
What is neural machine translation?
Neural machine translation is a technology based on networks of artificial neurons. The system detects correlations between the source and target language and can thus predict the probability of a certain word sequence.
How does translation software work?
There are various providers of translation programs that work with different methods. Some software breaks down each sentence word by word and looks them up in the dictionary. The most widespread method today, however, is neural machine translation, which can use artificial intelligence to make statements about the probability of a phrase being used. This system is used by leading translation services such as Google Translate or Bing. The provider DeepL has further developed the technology for itself.
Can machine translation replace human translation?
Machine translation systems are a good way to have many words translated cheaply in a short time. Nevertheless, you should realise that this method cannot compete with human translation in terms of quality.
Which texts are suitable for machine translation with subsequent post-editing?
Where automatic translations do not yet meet all requirements in terms of quality, post editing, i.e. subsequent revision of the text by a human, can close the gap. Texts with simple vocabulary, short sentences and few convolutions are particularly suitable for this. Post-editing delivers high-quality results and saves time and costs.
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