What is neural machine translation?

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 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 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 terminology should be created. 

• Function of the text and target audience: factors such as the function of the text or its target audience cannot be fully captured by 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, so it will not always provide the most appropriate translation.  

• Polysemy and emotional context: in the case of terms that may have more than one meaning, the use of a neural machine translator could lead to certain errors. The machine is also unaware of the emotional impact that different words or styles may have on readers.   

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 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.  

Using the statistical models on which statistical machine translation is based, huge amounts of data are entered and the software establishes patterns and relationships between words and phrases. However, in neural machine translation, machine learning comes into play, which leverages neural networks to enable the software itself to learn to translate autonomously. 

As a result, neural translation provides greater quality and coherence, because it understands the grammar and semantics of the texts. Its function is to learn to translate by itself, while statistical translation uses probabilistic models to generate translations based on patterns previously identified in large volumes of parallel data. Neural machine translation therefore offers translations with a broader vocabulary, more complex structures and can even handle unknown words. Therefore, NMT might be slower in certain contexts due to its greater computational complexity, but it tends to offer better quality translations since it uses more resources in its system of neural networks based on deep learning.

The pros of neural translation 

Machine translation based on neural networks is distinguished by its ability to interpret the context and intention of the original text more accurately than other automatic solutions.  Although they usually generate fluid results with fewer grammatical errors, it is always advisable to have a human review and adjust the final text to ensure maximum quality. 

A notable advantage of neural translation systems is their ability to improve and adapt over time. As these models are provided with more data and receive feedback, they are optimized, achieving increasingly accurate and fluent translations. This continuous learning means that the quality of translations is constantly improving with the use and incorporation of new linguistic data. 

Compared with traditional methods, neural models provide much greater scalability and flexibility. Thanks to their ability to learn linguistic patterns from large volumes of data, they can easily adapt to different languages and contexts. This makes them especially useful in multilingual environments and in specialized translation in fields such as medicine, technology and law. 

In short, neural translation is a significant advance in the field of machine translation, offering more accurate, natural and adaptable results. With its ability to capture context, streamline the localization process, reduce costs, improve over time and adapt to multiple languages and contexts, this leap forward in machine translation facilitates more natural and effective global communication.  

We hope you have found this information useful. We will be happy to provide you with more details about the different aspects of translation. Do not hesitate to contact us or visit our blog for more information.