High-quality translation is not just a mechanical transmission of words, but a whole art form. It does not involve simply replacing the words of one language with another, but a deep understanding of the context, nuances, and cultural characteristics of the original and target languages. It is a skill, which professional translators devote long years of study and practice to perfect.
At the same time, modern machine translation systems are developing as fast as lightning. Now they can absorb huge amounts of language data at a rapid speed, due to smart learning systems. As a result, machine translation can compete with the one made by humans quality-wise, while doing the task at a higher rate.
How does neural machine translation work?
The Neural Machine Translation is based on the neural networks, which imitate the functioning of the human brain. The main elements of NMT are encoders and decoders. The encoder converts the source text into a hidden representation (vector), and the decoder converts this vector into text in the target language. The attention mechanism plays a major role in NMT, allowing the model to focus on different parts of the source text when generating a translation. This helps to take better account of the context and improve the quality of the translation.
In simple terms, unlike earlier machine translation systems based on language rules or phrases, neural networks use the context of the entire original sentence, as well as a sequence of already generated translation words, to predict the next word. This approach is similar to the way a professional translates.
In addition, neural networks are able to quickly adapt to specific subject areas and text styles by learning from relevant data. This greatly increases their flexibility and applicability in different scenarios.
Benefits of Neural Machine Translation
Why has neural machine translation become so popular? Why is it difficult to imagine a modern translation without neural networks? Obviously, due to their advantages over other systems, namely in:
1) translation quality: The ability of neural networks to take into account the broad context of the sentence and text as a whole allows for higher translation results, especially for complex, multi-valued language constructions;
2) learning from big data: NMP can learn from huge amounts of data and parallel texts, which allows it to improve the quality of translation with each new stage of learning.
3) flexibility: NMT easily adapts to different languages and text styles. This greatly expands their scope and helps ensure high performance where it is essential.
History of Neural Machine Translation
Over the past 30 years, machine translation systems have gone through several stages of development. And with each new stage, the quality of translation results increased noticeably.
The first systems were based on linguistic rules and bilingual dictionaries, making them slow and not always accurate. In addition, taking into account all the rules and possible meanings of words turned out to be simply impossible.
These systems were replaced by statistical methods that improved the quality of translation by analyzing and searching for patterns in large volumes of parallel texts. They were good at translating successive phrases, but if it came to new or unfamiliar phrases, the quality of the translations rapidly decreased.
Finally, in the early 2010s neural machine translation can compete with the one made by humans quality-wise, while doing the task at a higher rate.
Unlike traditional machine translation systems, neural systems use artificial neural networks. These networks are trained in vast volumes of parallel texts in different languages, revealing complex relationships and patterns between words, phrases, and sentences. This provides even more accurate and native-sounding translations.
This creates an idea that neural networks are perfect systems and that we can rely on 100%, but this isn’t true at all. There are a variety of potential problems connected to the neural networks that should be taken into account.
Limitations of Neural Machine Translation Systems
Despite all the advantages, the use of neural machine translators involves some difficulties. Let's discuss some of them:
- translation errors and dependence on data quality: the quality of neural translation depends on the value of training data. Errors in data or inaccuracies can be directly transferred to the final results of translations. NMT may still make mistakes when translating rare languages, or specific terms.
- contextual and cultural aspects: NMTs sometimes find it difficult to capture the subtle cultural and contextual nuances of language. Despite their great potential, neural machine translators can still exhibit errors in the understanding of context, slang or ambiguous words.
- lack of transparency and unpredictability: The complexity and internal structure of neural networks makes it difficult to explain and interpret their solutions, leading to errors and unpredictable results in some cases.
Considering all the problems listed, it is recommended to double-check the translations made by neural machine systems to ensure their accuracy. The checking process must be carried out by a professional with knowledge of the field of the text.
Lingvanex advocates a balanced approach to the use of NMT technology, recognizing that, despite all the advantages, it has not yet reached the level of professional translation performed by a person. This applies in particular to texts of a legal, financial or medical nature where the cost of an error can have serious consequences.
Let's have a look at what popular translation solutions use NMT technology.
Overview of leading neural machine translators
1) Google Translate: One of the most famous examples of NMT. According to the company, the neural networks have significantly improved the quality of translation, which is also confirmed by millions of users around the world.
2) DeepL: a German company that has developed one of the most accurate translators based on neural machine translation. Their system is capable of translating texts with high accuracy while taking the context into account.
3) Microsoft Translator: Applies to various company products, including Office and Skype, which promotes communication in different languages.
4) Lingvanex: Machine translator based on artificial intelligence and neural networks of the Transformer architecture. These networks are based on attention mechanisms and positional coding. When translating, context is taken into account, since the model remembers what happened at the beginning of the long sentence and does not forget this information by the end of the phrase.
The future of neural translation
Neural machine translation continues to evolve and improve through modern research and its constant development. Here are a few current trends in NMT development in the near term:
1. creation of multilingual models: new models are trained in several languages at the same time, which improves their ability to provide translation for different language pairs. The more these language pairs are mastered by the system, the more effective neural machine translators are.
2. integration with artificial intelligence: the usage of NMT together with other AI technologies such as natural language processing (NLP) and speech recognition is currently increasing. Lingvanex actively uses this technology in its local software to decrypt speech in 91 languages.
3. improving quality: the work on improving the translation quality continues, including the development of its ability to meet high-quality standards while dealing with rare languages, slang expressions, and specialized terms.
Conclusion
Thus, neural machine translation is a powerful tool that is already helping millions of people around the world overcome language barriers. According to the current trends, despite some limitations and problems, technologies based on neural networks are still actively developing, contributing to the improvement of the quality and efficiency of neural machine translators. The future of neural machine translation promises even more opportunities for successful communication between people who speak different languages.
