Types of machine translation (and how to choose the right one)

What is machine translation?

Simply put, machine translation (MT) is translation done by software instead of a human. It takes text in one language and automatically converts it into another, using different kinds of technology under the hood.

You’ve probably seen it in action if you’ve ever used Google Translate, watched instant subtitles on YouTube, or clicked “See translation” on an Instagram post. It’s fast, scalable, and getting better all the time.

Why are there different types of machine translation

Some texts are highly technical. Others need to feel personal, persuasive, or culturally precise. That’s why different types of machine translation exist in the first place: to handle different needs, levels of complexity, and quality expectations.

Early systems followed strict grammar rules. Then came models that relied on statistics and patterns. Today, most tools use neural networks trained on massive amounts of data to produce fast, fluent translations. This is what powers modern MT engines like DeepL and Google Translate.

So if neural machine translation is the best, why even talk about the other types? Because understanding how we got here helps you understand where NMT still struggles (and also where older or hybrid systems still make sense). 

Let’s take a closer look.

Rule-Based Machine Translation (RBMT)

Rule-Based Machine Translation was one of the first approaches to machine translation. As the name suggests, it works by applying a set of linguistic rules and bilingual dictionaries to convert one language into another.

Think of this machine translation type like an old-school language teacher. It knows the grammar inside out and follows the rules strictly, but it doesn’t adapt well to casual phrases, tone, or real-world context.

When RBMT makes sense:

• For controlled environments where the language is very structured
• When working with highly technical (or legal content) where rule precision matters
• In older systems or regions where NMT isn’t available

Where it falls short:

• Natural, conversational language
• Speed and scalability
• Handling idioms or informal tone

Today, RBMT is rarely used on its own. But some hybrid systems still build on it to improve output quality.

Example-Based Machine Translation (EBMT)

Example-Based Machine Translation works a bit like a phrasebook. It doesn’t follow rules or crunch stats. Instead, it looks for similar sentences or fragments from a database of past translations and reuses them to build the new one.

It’s kind of like saying: “We’ve translated something like this before. Let’s tweak it and use it again.”

This type of machine translation system breaks the input into chunks, finds the closest matches in the memory, and pieces together the result. The more examples it has to work with, the better it performs.

When EBMT makes sense:

• When you have a large database of high-quality, reusable translations
• For industries with lots of recurring phrases (e.g., software UI, manuals)
• As a building block for hybrid or translation memory systems

Where it falls short:

• Doesn’t generalize well to new or unpredictable content
• Can struggle with fluency if reused chunks don’t fit smoothly
• Lacks the “learning” and adaptability of newer MT systems

EBMT isn’t widely used on its own anymore, but its influence lives on in tools like translation memory and some hybrid machine translation setups.

Statistical Machine Translation (SMT)

Statistical Machine Translation took things a step further. Instead of relying on hand-coded grammar rules, SMT is a type of machine translation that uses large volumes of bilingual text to learn how words and phrases typically map between languages.

It’s basically data-driven translation. The system looks at millions of translated sentences, crunches the numbers, and predicts the most likely translation based on patterns in the data.

When SMT makes sense:

• In low-resource settings where neural models haven’t been trained yet
• For quick implementation when rule-building isn’t practical
• When you have a large, domain-specific dataset (like medical or technical manuals) that can improve accuracy

Where it falls short:

• Fluency and natural-sounding output
• Understanding long sentences or context
• Maintaining tone or brand voice

Interlingual Machine Trasnlation

Instead of directly translating from one language to another, Interlingual Machine Translation first converts the source text into a language-neutral meaning layer. This layer is called an interlingua. Then, it generates the target language from that universal representation.

You can imagine the machine translation system pausing the translation halfway through to say, “OK, what does this actually mean?” Then, thanks to the way it’s built, they have everything they need to recreate the same meaning in the new language.

In theory, if you’d translate every language into the interlingua, you’d only need one set of rules for each language (instead of hundreds of direct language pairings).

When interlingual MT makes sense:

• In multilingual systems where content must go through a central, standardized format
• In research and academic environments exploring semantic or AI models
• As a concept behind certain AI-driven content generation systems

Where it falls short:

• Very hard to build and maintain
• Struggles with nuance, tone, and ambiguity
• Rarely used in production systems today

Keep in mind that interlingual MT is more of a theoretical or academic model than a commercial one. But it did influence how modern types of machine translation (especially neural models) thinks about meaning, context, and structure.

Neural Machine Translation(NMT)

Neural Machine Translation is the current gold standard in machine translation. It uses deep learning and artificial neural networks to produce translations that are fast, fluent, and human-like.

Instead of translating word by word or phrase by phrase, NMT looks at the entire sentence at once, understands context, tone, and even idioms. That’s why it generates translations that often feel more natural, compared to the output of other types of machine translation. 

If you’ve used DeepL, Google Translate (after 2016, to be precise), or Lokalise AI, you’ve already seen NMT in action.

When NMT makes sense:

• Perfect for most modern translation needs (especially at scale)
• When fluency, tone, and context matter (like marketing or customer support)
• As a base layer in human-in-the-loop workflows or post-editing workflows

Where it falls short:

• Niche domains with very technical or sensitive content
• Languages with limited training data (truth be told, this is improving fast)
• Nuance that requires cultural or brand-specific awareness

The big upside? NMT continues to learn and improve over time. With the right setup, it’s fast, affordable, and powerful. Plus, there are many ready-to-use machine translation and AI translation tools that you can implement on a budget.

When to use which type

By now, you’ve seen that not all machine translation is created equal. But how do you know which type is right for your project?

Check out this table for a quick way to think about it.

As you can see, neural machine translation is the most promising type. Limitations don’t matter that much considering if 1) you consider that it’s continuously improved and 2) you have a human overseeing the process and doing quality assurance.

Limitations of machine translation

Even the most advanced systems, like neural MT, can struggle in certain situations. If you’re relying on MT without understanding its limits, you risk poor translation quality. Nobody wants that.

From our experience, when you’re aware of the limitations, you can navigate using machine translation and AI translation tools with more confidence.

Context and nuance

Machine translation systems can miss implied meanings, sarcasm, or subtle cultural cues. What makes perfect sense in one language may fall flat in another.

Brand tone and voice

If you’re translating marketing copy, slogans, or UX content, MT often fails to capture your brand’s personality. You might get the words right, but the feel is off. Luckily, some AI translation tools allow you to “feed” context to them, which improves the output significantly.

Low-resource languages

Languages with fewer available datasets may not perform well in NMT engines. The result? Inconsistent or clunky output.

Sensitive or regulated content

Machine translation can introduce small inaccuracies that can turn into big problems. This is especially true for legal, medical, or financial content where precision is non-negotiable.

Formatting and design issues

Machine translation doesn’t always play nicely with layouts because it lacks visual context. And so, translated text can break UI elements, expand too much, or throw off your visual design.

While some of these limitations are likely to be minimized over time thanks to technological advancements, there is one truth that you need to hear. Here it goes:

Human input is still relevant (and often critical) in the translation workflow.

How human editing complements machine translation

Post-editing is the step where a human translator reviews and refines machine-generated content.

Sometimes that means fixing a few clunky phrases. Other times, it means rewriting entire sentences to make sure they actually make sense, sound natural, and align with your tone.

Here’s what a human editor brings to the table:

• Fixes small errors that machine translation tools might miss
• Makes the text sound natural and fluent
• Keeps your tone and brand voice on point
• Handles slang, idioms, and cultural nuances
• Ensures consistency across your content
• Brings in domain knowledge when it matters

So, it’s not a question of “either-or”. You should use machine translation tools and help your translators work more efficiently. Let the tech do the heavy lifting, and then the linguists can apply all the fine touches.

Don't just pick a system, pick a strategy

Although it might seem like it, choosing a machine translation system isn’t a technical decision. It’s a strategic one.

Instead of asking “Which machine translation type is best?”, it’s far better to ask the following:

“What does my content need? Why are we translating this in the first place?” 

Then look inwards and explore. Is speed more important than tone? Do you need domain accuracy, or just rough understanding? Will a human step in to thoroughly review it, or is the output going to production with minimal quality assurance?

The answers to these questions will guide your setup.

• Use NMT for most modern workflows (it’s fast, fluent, and widely supported)
• Add human editing where tone, accuracy, or context really matter
• Explore hybrid setups or domain-trained engines for specialized use cases
• Think about scale, automation, and how machine translation fits into your larger localization flow

As the last takeaway, remember this. The best translations come as a consequence of building the right systems. This includes tools, people, workflows, and procedures. We know this very well, and that’s why we built Lokalise—to bring all of these elements together.

Want to learn more about machine translation, large language models (LLMs), and AI in translation? Visit Lokalise blog.