Language, the Basis of Neural Machine Translation

EXPERT MACHINE TRANSLATION

Neural machine translation has enabled astonishing advances in natural language processing. This is a machine translation model that uses neural networks to interpret an input sentence in one language and generate the equivalent sentence in another.

The method differs greatly from previous ones because its neural network is fed with a huge volume of data and uses language models in order to understand and adapt to context. Said network encodes and decodes the input and output text. This leaves statistical machine translation behind and opens many doors for faster and more accurate natural language processing.

Neural machine translation technology is constantly learning in order to improve its accuracy and increase its flexibility. This impressive evolution is all down to artificial intelligence (AI).

The relationship between machine translation and artificial intelligence

Artificial intelligence has, without a doubt, been the driving force of the natural language processing advances that have occurred in machine translation. Although these translation systems are not yet capable of capturing nuances and details in texts quite like a professional translator would, AI has provided them with the ability to produce near-human quality translations.

Thanks to artificial intelligence, machine translation uses advanced algorithms that process large amounts of information, making it possible to operate with minimal human intervention and providing greater NLP accuracy through model training.

Of course, revisions by human translators are still necessary, especially when dealing with fixed expressions specific to each language or when translating highly creative content, such as literary texts.

Using artificial intelligence and neural networks, machine translation systems are able to interpret human language in a deeper way. In addition, AI-based models can be created in order to perform other linguistic tasks, such as text classification, answer generation, summarization, etc.

What is machine learning in AI?

Machine learning is the process in which a data set is analyzed using artificial intelligence to detect hidden trends or to make predictions. The objective is to transform such data into useful information that will enable decision-making.

The machine learning used in machine translation can be supervised or unsupervised. We speak of supervised learning when a human being—an AI developer, specifically—guides the process to tell the algorithm what to learn (labeled data).

On the flip side, there is unsupervised learning, a process in which hidden and complex patterns are detected, but without the need for AI developers to supervise or guide the procedure. This type of learning is often associated with modern artificial intelligence.

In addition, there is also reinforcement learning, a type of learning that consists of rewarding desired behavior and penalizing undesired behavior. It is a type of learning based on trial and error.

Related:

Human-in-the-loop (HITL); making the most of human and machine intelligence

How has machine learning evolved?

The evolution of learning in the field of artificial intelligence has led to the application of new technologies and has given rise to powerful innovation, such as the Internet of Things (IoT).

After great innovations such as the first computer, theoretical and analytical equipment proposed by Charles Babbage in 1834; the creation of Boolean logic in 1847 (with operators that are still used today), and the electromechanical machine developed by Hollerith in 1890, the first neural network model was created in 1943. This is the basis of machine learning and deep learning.

In 1954, the first machine learning model was developed and, in 1967, machines acquired the ability to detect patterns. In the 1980s, this learning evolved into supervised learning.

In the 1990s, applications based on machine learning were created for web applications, language learning, word processing, etc.

Later, this learning gave rise to a more complex model: the neural networks learning model.

Read more:

Techniques for Measuring Machine Translation Quality

Neural machine translation: industries and applications

Some of the applications of neural machine translation include:

Translating large volumes of content in a short amount of time, with high quality and without human assistance. It may be of particular interest to companies that need immediate translations. For example, the Red Cross, an organization that needs to report on events as they are developing.
Translating repetitive content, such as user guides or product manuals.
Translating online customer requests, such as in customer service or technical support departments, especially if the company uses live chat systems.

Neural machine translation is the perfect technology for institutions and companies that need to perform demanding natural language processing tasks and want to produce high-quality, flexible, fast, and accurate translations.

Pangeanic is a leading natural language processing company, specialized in neural machine translation, with experience in banking, financial, healthcare, and legal industries, among others. We collaborate with national and international companies around the world.