How Machine Learning Changing The Translation Industry?

How Machine Learning Changing The Translation Industry

The translation industry is one of the oldest and most essential in the world. It has been a key player in globalisation, bridging the language barrier and allowing for the free flow of information across borders.

With the advent of new technologies, the translation industry has undergone a major transformation. Machine learning is one of the most disruptive technologies to hit the translation industry in recent years.

In this article, we’ll look at how machine learning is changing the translation industry and what it means for translators.

How Machine Learning Is Changing The Translation Industry?

The translation industry is undergoing a major transformation due to the advent of new technologies, one of the most disruptive being machine learning.

Machine learning is artificial intelligence that allows computers to learn from data without being explicitly programmed. This technology is revolutionizing many industries, and translation is no exception. It is based on algorithms that learn from data to identify patterns and make predictions.

Machine learning has been around for decades, but it only recently began to be used on a large scale due to advances in computing power and data storage capacity.

The translation is a perfect industry for machine learning because it involves a lot of data that can be used to train algorithms.

Machine learning (ML) consulting services can be used for various tasks in translation, such as automatic translation, quality estimation, and text recognition.

1. Automatic translation

Machine learning can be used to build automatic translation systems, also known as machine translation (MT). MT systems are not new, but they have greatly improved in recent years due to advancements in machine learning.

MT systems work by translating text from one language to another using a computer program. They usually require professional translation services of humans to create a translation memory, a database of previously translated texts. The MT system then uses this memory to translate new texts.

2. Quality estimation

Quality estimation is the task of predicting the quality of a translation before it is even completed. This is useful for translators who want to know how accurate their translations are likely to be.

There are two main types of quality estimation: human-based and machine-based. Human-based quality estimation relies on humans to assess the quality of a translation. This is usually done by having multiple translators translate the same text and then compare their translations.

On the other hand, machine-based quality estimation uses machine learning algorithms to predict the quality of a translation. This is done by training the algorithms on a dataset of translations that humans have rated.

3. Text recognition

Text recognition is the task of extracting text from images. This is useful for translating documents that are not in digital forms, such as scanned books or handwritten notes.

Text recognition can be done using optical character recognition (OCR), a machine learning type. OCR algorithms are trained on text images and learn to recognize pixel patterns. They can then convert the images into digital text that can be translated using an MT system.

Impact Of Machine Learning On The Translation Industry

So far, machine learning has had a major impact on the translation industry in three main ways:

  1. Machine translation
  2. Computer-aided translation
  3. Translation quality assessment

1. Machine translation

Machine translation is one of the most well-known applications of machine learning in the translation industry. This technology allows for the automated translation of texts by computers.

While machine translation has been around for many years, it has only recently become truly viable due to the power of machine learning. The most popular machine translation engine today is Google Translate.

Google Translate uses a neural machine translation system that relies on deep learning algorithms to provide accurate translations. These algorithms are constantly improving as they are fed more data, allowing Google Translate to provide more accurate translations.

While Google Translate may be the most well-known application of machine learning in Translation, it is far from the only one. Many other machine translation systems on the market, such as DeepL and SYSTRAN, are powered by different forms of machine learning.

These machine translation systems have had a major impact on the translation industry by making it possible to translate large volumes of text quickly and cheaply.

In the past, translations were done mostly by human translators working alone or in small teams. But with machine translation systems, it is now possible to translate entire documents in just a few minutes using nothing more than a computer.

2. Computer-aided translation

Computer-aided translation (CAT) tools are software that helps translators be more efficient and consistent in their work.

Traditionally, CAT tools have been used to store and manage translation memories – databases of previously translated texts that can be reused for future projects. However, the latest generation of CAT tools uses machine learning algorithms to go one step further.

Some of the latest CAT tools on the market use machine learning to provide real-time suggestions to translators as they work. This can be by suggesting similar phrases that have been translated or offering alternative translations for a given the word or phrase.

3. Translation quality assessment

Translation quality assessment (TQA) is the process of assessing the quality of a translation, typically using some sort of metric or checklist.

In the past, TQA has been a manual process carried out by human reviewers. However, machine learning is beginning to change this.

There are now a number of software tools on the market that use machine learning algorithms to automatically assess the quality of translations. These tools can check things like grammar, spelling, and style consistency and provide a score or rating for the translation.

Of course, machine translation systems are not perfect. They often make mistakes that human translators would never make, such as mistranslating idiomatic expressions or using incorrect grammar. As a result, many companies still prefer to use human translators for important documents where accuracy is paramount.

But even with their imperfections, machine translation systems have enabled businesses to communicate across borders like never before. And as these systems continue to improve, they will likely have an even bigger impact on globalization in the years to come.

What Does This Mean For Translators?

The rise of machine translation has led some to believe that translators will soon become obsolete. They argue that machines will eventually be able to translate as accurately as humans, if not better.

While it is true that machines are getting better at translating all the time, there are still many situations where human translators will be needed even when machines are available.


The translation industry has undergone a major transformation in recent years because of the advent of machine learning. Machine translation systems can now translate entire documents in just a few minutes, and the latest generation of CAT tools uses machine learning to provide real-time suggestions to translators as they work.

To stay relevant in the age of machine translation, translators need to keep up with technological advances and learn how to use CAT tools and machine learning algorithms to improve their workflows.