The evolution of machine translation technology is now far-reaching. This interdisciplinary field - which combines linguistics, computer science, and artificial intelligence - has accelerated and made great strides! We have moved on from rule-based machine translation systems to more complex and sophisticated approaches - including machine learning and neural networks. Whether it's facilitating communication between people of different languages and cultures, improving productivity or personalization. Machine translation offers many advantages. All these advances are opening up new perspectives and transforming the way we access information, interact and share content on a global scale. So what are the advances and developments in this field? Why is the evolution of machine translation technology essential in an intensely international business environment? And what are its advantages? In this article, we examine the evolution of machine translation technology, its current applications, and the challenges it still faces.
At Streamr, the speech-to-text specialist, automatic speech recognition technology is at the heart of our business. As a result, we take a keen interest in more or less related technological advances, such as those in machine translation. In a nutshell, according to IBM, automatic speech recognition (ASR) - or speech recognition is a tool, a "functionality that uses natural language processing to transcribe human speech into a written format". It is by processing language that it comes close to machine translation.
However, as AWS Amazon points out, machine translation is the "process of using artificial intelligence to automatically translate text from one language to another - without human intervention". So, although these two technologies are different, they are similar, in terms of system development, research, and advances. Their common denominator is the concept and process of machine learning.
Machine learning is undergoing rapid growth and progress in three key areas:
-Technological advances.
-Increased computing power.
-Abundance of data.
Indeed, one of the main driving forces behind the growth of machine learning is the increasing availability of data. With the increasing digitization of our daily and professional activities in recent years, vast quantities of data are being generated across all sectors. It's this data that drives these machine-learning models ever more accurate and powerful.
When it comes to translation, individuals and companies can now rely on much more powerful machine translation algorithms.
Machine translation and machine learning are closely linked to artificial intelligence. The impact of AI on the translation market is significant. The introduction of machine translation and computer-assisted translation (CAT) is helping professional translators to become faster and more efficient. For example, these tools offer automatic suggestions by reusing segments of previously translated texts. In addition, machine translation can detect common errors and misunderstandings, and facilitate and improve translation quality.
Overall, thanks to the emergence of deep neural networks and neural translation models, AI-based translations are increasingly accurate, fluent, and consistent. Secondly, artificial intelligence can automate a large part of the translation process, and is open to a wide range of languages - enabling ever-greater adaptation and personalization.
In concrete terms, here are the latest and most noteworthy advances in machine translation:
Neural translation models: Neural Machine Translation (NMT) is a technology based on artificial neural networks. It has made considerable progress in recent years thanks to artificial intelligence, and can now be used as a foundation for certain professional translations. NMTs are models that have replaced traditional statistical machine translation methods. These models use layers of neurons to capture the complex relationships between words in a source language and generate what are described as more natural, fluid translations;
The Transformer Model: the Transformer model was introduced in 2017 and has revolutionized machine translation. This model uses an attention-based neural network architecture - enabling translation models to take contextual words and syntactic dependencies into account more effectively;
Multilingual learning: recent advances have enabled the development of multilingual machine translation models capable of translating between several languages;
Reinforcement learning: this approach involves training a translation model through a series of iterations - with penalty or reward depending on the quality of the translation produced. It is a process that is constantly repeated in order to improve.
In the field of new technologies and automation - particularly for machine translation - the challenges and prospects are numerous. First of all, translation can be improved through what is known as "active learning". This involves using human interaction to guide the machine-learning process of a machine translation model. The model can ask the human user for specific translations and quality assessments. This will enable the model to improve itself on the basis of relevant examples. In this process, human review is important and necessary in order to adjust translations - taking into account contextual considerations, nuances, the subtleties of a professional field, etc.
Conclusion
Finally, this advanced technology makes it possible to set up a high-quality multilingual subtitling project - without the costs that can be incurred by translations carried out entirely by professional translators. At Streamr, our translation standard combines the performance of this technology with the help of our ancestor's translations. In short, machine translation, driven by current technological upheavals and artificial intelligence, is still in its very early stages and many developments are underway.
Facebook
Instagram
Twitter
Linkedin
Youtube