Machine Translation (MT)

What is Machine Translation?

Machine translation (MT) is a term used to refer to using algorithms and machine learning (ML) models to translate natural language text from one language to another.

In this context, the input language can be referred to as the source text, and the translated language is known as the target text.

How Does Machine Translation Work?

Machine translation solutions break down input text into words and phrases and translate these components into the target languages without the need for human assistance.

While there are many different approaches toward machine translation, one of the most widely-used approaches, neural machine translation, involves training a neural network on a large database of existing translations so that it can predict the most likely sequence of words.

5 Types of Machine Translation

There is a wide range of approaches that organizations can take toward machine translation. Some of the most common are outlined below.

1. Rule-Based Machine Translation?

Rule-based machine translation is a classic approach to translation where a human language expert defines established rules for language and structure, which establish how input text should be translated.

2. Statistical Machine Translation?

Statistical machine translation is a solution that uses statistical models to look for patterns in existing translations and use them to translate the text on a word or phrase-based basis.

Using a phrase-based approach helps to translate entire sequences in context rather than just individual words.

3. Neural Machine Translation

Neural machine translation is a type of machine translation that uses neural networks to translate the source text. These solutions can infer the context of words and phrases and translate with high accuracy.

Examples include Google Translate and Baidu Translate.

4. Hybrid Machine Translation?

Hybrid machine translation is where multiple translation techniques are used as part of a unified solution.

For example, a system could include rule-based, statistical, and neural translation together to maximize the accuracy of the translation.

5. Example-Based Machine Translation?

Example-based machine translation is an approach to translation by analogy where a system is provided with a series of sentences and a series of approved translations in the target language.

This means that when the user inputs a certain sentence, the system can automatically translate it to the correct translation.

What’s the Point of Using Machine Translation?

Translators and organizations use machine translation systems to streamline the translation of source texts into hundreds of different languages.

According to Precision Reports, the global machine translation market was valued at $847.24 million in 2021 and is estimated to reach $2,107.56 million by 2027. Part of the reason for the value of the market is that machine translation offers a much more scalable approach to translating text than relying on a human translator.

However, while many solutions are highly accurate, they lack the ability to infer context in the way that humans can. As a result, many organizations opt to use machine translation to translate texts in bulk and create a preliminary translation before a human translator proofreads the texts for accuracy.

How Accurate is Machine Translation?

The accuracy of machine translation depends not just on the type of translation technique in place but also on its execution. Generally, machine translation can be considered to be slightly less accurate than human translation.

Weglot, a translation service, produced a study reviewing five leading machine translation technologies (Amazon Translate, DeepL, Google Cloud Translation, Microsoft Translator, and ModernMT) to test their effectiveness at translating website content and found that 85% of the sample reviews were very good or acceptable in terms of usability.

The study also found that? “website translations by contemporary NMT are highly usable and require mostly minor editing.” That being said, the study did not show the tendency of machine learning translation systems to translate certain texts out of context.

This highlights that machine translation works best when proofread by a human user. This way, organizations can ensure accurate translations are produced at pace in the context of the original text.