What Is Machine Translation and How Does It Work?

Machine translation is the use of computer technology to translate text from one language to another.

Humans have been tinkering with the technology since 1940, improving processes incrementally over time.

In this article, we’ll define machine translation, dive into the mechanics behind it, and try to resolve the human vs machine controversy.

• Machine or automated translation is the process whereby computer software takes an original (source) text, splits it into words and phrases (segments), and finds and replaces these with words and phrases in another language (target).

• There are four main machine translation methods: rules-based machine translation, statistical machine translation, neural machine translation, and hybrid machine translation.

• Smartcat's global content platform uses AI agents to perform specialized machine translations for various content types.

Machine or automated translation is the process whereby computer software takes an original (source) text, splits it into words and phrases (segments), and finds and replaces these with words and phrases in another language (target).

Using various algorithms, patterns, and large databases of existing translations, machine translation technology can quickly produce large quantities of translated texts without any human involvement at all.

Different types of machine translation software use different mechanisms to find the best matches for source words and phrases to produce comprehensible text in the target language.

There are four main machine translation methods:

• Rules-based machine translation applies specific language rules and vocabularies, created by language experts in a similar way to how we apply grammar and syntax rules when creating texts in our native languages.

• Statistical machine translation relies on analyzing enormous amounts of existing human translations to find the closest analogue of the target segment. As a result, it can have some issues with language pairs where the phrasing is completely different.

• Neural machine translation learns to translate using a large network of resources such as databases, glossaries, and translation suggestions previously approved by translators.

• Hybrid machine translation usually uses statistical and neural types at the same time. Companies use this method as a fail-safe way of delivering accuracy and assuring control, instead of relying on only one solution.

So why, with all these advancements, hasn’t machine translation replaced human translators yet?

You can’t deny the fact that machine translation is becoming increasingly popular, not only with businesses of all kinds but also with language service providers.

What about the quality? With the right approach, neural machine translation can compete with humans. For example, our post 7 Easily Applicable Predictions for B2B Digital Marketing in 2022 was localized from French using Smartcat technology that features machine translation.

But still, both human and machine translation are alive and kicking, and here to stay. Machine translation and AI translation software, though massively helpful in improving translators’ productivity as well as translating large volumes of texts, does not always meet the quality standards expected of translation services.

This is why, in most cases, human language professionals are tasked with MT post-editing to make sure the result sounds natural and is accurately localized for target audiences.

So, the winning formula seems to be combining the two: machine translation first, then post-editing by a human linguist.

There are many machine translation tools and they all have their own technology and algorithms. Here’s a quick overview to compare features and find which could work best for your needs.

Smartcat is an AI platform for multilingual content creation, translation, localization, and publishing. Smartcat's AI agents are trained and enabled by your expert employees–automating the entire content lifecycle and continuously learning from human feedback to deliver consistent, compliant, and high-quality content in every language and market.

Google Translate was the pioneer in neural language processing. This, combined with a wide range of languages and integrations make it the leading translation engine, at least in Europe and the US.

Also neural-based, Amazon Translate works closely with Amazon Web Services (AWS). Surprisingly, it’s said to be more accurate than Google Translate in certain languages, like Chinese.

Predictably integrated with Office and other Microsoft products, Microsoft Translator is another popular cloud-based neural engine. It provides instant machine translations within documents and other software tools.

Powered by a small provider based in Germany, DeepL is a machine translation engine that is believed to produce more nuanced and natural translations thanks to its proprietary neural AI.

Most translation tools on the market have one or several translation engines available for their users. For example, Smartcat uses a library of expert-enabled AI agents that specialize in translation and creation of various content types.

Whatever you use when it comes to machine translation, the best part is that it can only get better. The ever-growing need for content localization will continue to push machine translation technology forward to the benefit of everyone involved, whether businesses, language professionals, or end-consumers.