We have been hearing about machine translation and its different forms for several decades now: statistical, rule-based and, more recently, as technology has evolved, neural machine translation, which has made significant progress in this field and is continuing to make constant progress. Statistical and rule-based machine translation do not fully understand grammar and semantics, unlike neural translation.
What is neural translation
Neural translation or machine translation based on neural networks, otherwise known as Neural Machine Translation (NMT), is a technology that uses artificial intelligence to achieve high-quality and accurate translations, surpassing conventional machine translation methods.
Using artificial neural networks, this technology allows for the generation of reliable and accurate translations through a continuous learning process. The more these systems are used, the more they learn, feeding off data extracted from other translations. These neural networks attempt to mimic the functioning of the biological neural networks that make up the human brain.
How do artificial neural networks work
A machine that performs translations bc data brazil similar to those performed by humans has a fairly fast learning system. In this case, deep learning is used, which involves training the system with a large amount of data so that it improves for future texts in terms of terminology and technical terms, among other aspects.
The neural translation system feeds on a wide range of data coming from terms, phrases and texts that have already been translated. With this information, the machine learns to interpret different textual elements and their contexts, trying to decipher and, to a certain extent, predict in which thematic environment or situation the text will be used. This results in translations with a high degree of accuracy and quality, often comparable to those carried out by a human.
However, it is important
to recognize that while neural translation is superior to traditional machine translation, it is not perfect. The biggest difficulty is that it produces very correct sentences, which makes it difficult to detect errors. It should therefore always be complemented with post-editing by a professional human translator (preferably a specialist in the subject) to ensure the accuracy of the translation.
Challenges of neural translation
It is clear that neural translation offers multiple advantages, but it also poses several challenges that we should be aware of. Of these, the following are particularly worth highlighting:
Excessive literalness
sometimes, the outcome of a neural translation process can be an overly literal text, since a machine is unable to interpret and understand nuances like double meanings, metaphors, word games and other rhetorical figures. If we try to translate a joke with a machine, it will most likely not be funny, while a person will be able to adapt it to the situation.
Insufficient technical corpus: often, and you can also establish schedules depending on the language pair, a machine may. Lack an adequate technical corpus to be able to interpret highly technical texts or those with specialized terminology. In these cases, if the translation volume should justify it, specific translation engines trained with this.
Function of the text and target. Audience: factors such as. The function of the text. Neural machine translation.
Tone and register: software is also unable to detect the tone of the text, the register or the different cultural elements that compose it,
Despite all these challenges
neural translation is a highly effective system. In certain areas, it can boost productivity, reduce lead times, and lower costs– an increasingly essential america email list capability given the vast amount of information generated and translated today. However, professional translators or post-editors, who are responsible for editing and revising the text, are still essential in. Correcting possible translation errors and also adapting the text according to its. Purpose and target audience.
Difference between statistical translation. And neural translation
The main difference is that neural translation uses. Neural networks to learn and generate translations in a more dynamic and contextualized way, while statistical machine. Translation (SMT) relies on the analysis of linguistic data to identify translation patterns.
