Mirela
Neural Machine Translation is a method of machine translation that uses deep neural networks to convert text from one language into another. It learns translation behaviors directly from large datasets of bilingual or multilingual text, which means it doesn’t translate word by word. NMT actually attempts to understand the meaning behind a sentence so it can generate natural-sounding output in the target language.
In this guide, you’ll learn more about what NMT is, how it works, why it matters, and how you can leverage it in localization.
Why NMT is now the standard
Before NMT, we had translation systems that relied on statistical patterns or linguistic rules. These systems often failed when the sentences became long, complex, or idiomatic. But when NMT entered the scene, it reinvented it. And that’s because NMT models began treating translation as a holistic understanding task, and they could capture the context, structure, and meaning all at once. The translations sounded more human-like, which is what we all want.
NMT also excels because it learns continuously and scales effectively. Once you train the NMT, you can fine-tune it with additional data, teach it new terminology, or adapt it to specialized fields. You don’t have to rewrite rules or rebuild complex phrase tables because the model simply can absorb new patterns during training. NMT is far more sustainable and future-proof compared to the older systems.
GPUs and TPUs also played a huge role in making NMT the new standard. Neural networks are computationally demanding, as you probably know, and only in recent years has hardware become fast enough to train large models at scale. Thanks to breakthroughs in architectures like Transformers, the technology suddenly became better and practical for real-world use. You now get high accuracy and low latency, so it’s only natural that NMT is currently the go-to approach for modern translation platforms.
NMT vs. other translation methods
To really appreciate why Neural Machine Translation has become the dominant approach, it helps to compare it with the translation methods that came before it: Statistical Machine Translation (SMT) and Rule-Based Translation (RBMT).
| Feature | NMT | SMT | RBMT |
|---|---|---|---|
| Core method | Deep neural networks | Statistical probability tables | Hand-written linguistic rules |
| Natural fluency | Excellent | Moderate | Poor |
| Context handling | Strong (sentence-level or more) | Limited (phrase-based) | Very limited |
| Idioms | Often correct | Often literal | Almost always literal |
| Data requirement | Very high | High | Low |
| Adaptability | Very high | Low | Very low |
| Multilingual support | Strong | Moderate | Weak |
| Error types | Overconfidence, hallucinations | Missing context | Literal, rigid |
| Training complexity | High | Moderate | Low |
How Neural Machine Translation works
NMT happens in a few steps that can seem intimidating at first, but they’re actually easy to understand:
- Encoding the input sentence
- Neural network processing
- Decoding the target sentence
NMT works by transforming a sentence into a rich numerical representation, processing that representation with a neural network, and then generating a fluent translation in the target language. When you enter a sentence, the NMT system first breaks it down into words or subwords and converts them into vectors (mathematical forms that capture meaning, structure, and context). This step, known as encoding, allows the model to grasp not just the surface-level words but also how they relate to one another.
Once the sentence is encoded, the neural network (usually a Transformer architecture) takes over. Transformers use self-attention mechanisms that let the model examine every word in relation to every other word at the same time, helping it capture long-range dependencies, idiomatic expressions, and subtle nuances.
After processing the input, the decoder begins generating the translation one token at a time, using the encoded information and the words it has already produced to ensure the translation flows naturally. The decoder evaluates many possible next-word candidates and selects the most probable sequence, often using techniques like beam search to maintain fluency and grammatical correctness. That’s it!
Common NMT architectures
When you look at how Neural Machine Translation systems have evolved, you’ll notice that the architectures behind them have advanced quite a lot over the years. Early NMT models relied on Recurrent Neural Networks (RNNs), but these processed sentences one word at a time and struggled with long sentences.
To address this limitation, researchers introduced Long Short-Term Memory (LSTM0 and Gated Recurrent Unit (GRU) networks. These architectures included specialized gating mechanisms that helped them retain important information over longer distances, so the accuracy of the translation for more complex sentences could be improved.
It was an improvement, but the real revolution came with the introduction of the Transformer architecture. Transformers don’t process text sequentially. Instead, they use a mechanism called self-attention; the model looks at all words in a sentence at the same time and understand how each word relates to every other word. Today, almost all NMT systems are built on Transformer models or enhanced variations of them.
Types of NMT systems
Not all NMT systems are designed for the same purpose, which is why we have:
- General-purpose model.
- Domain-adapted models.
- Custom NMT models.
The general-purpose model (like Google Translate, DeepL, Microsoft Translator, and popular language-learning apps) is the most common one. These systems are trained on massive multilingual datasets and as the name suggests, they can handle all sorts of requests like everyday conversations, public-facing websites, emails, and simple documents.
Then we have the domain-adapted models, which are versions of neural translators fine-tuned for specific industries or subject areas. They are incredibly useful in fields like medicine, law, finance, engineering, and scientific research, where terminology needs to be extremely precise. Naturally, these are trained on specialized datasets and learn the conventions and vocabulary specific to the domain. As a result, they produce much more reliable translations than generic systems in these contexts.
Some organizations rely on custom NMT models, built and trained strictly for their internal needs. These models are usually tailored with proprietary datasets, so the system reflects the company’s preferred wording and stylistic choices. They work better at ensuring consistency across regions, maintaining brand voice, and handling niche terminology that general-purpose models may not recognize. However, building a custom model does require resources and technical expertise.
The main features of NMT
First and foremost, NMT understands context in a way that previous translation system could not. It examines the entire sentence (or even multiple sentences) to determine the most appropriate translation, and that’s why it delivers better outputs. The model is able to pick up on subtle cues like tone, intent, and implied meaning, all of which wasn’t possible not so long ago.
Another thing that’s impressive about NMT is its end-to-end learning framework. When the model sees enough bilingual data, it figures out linguistic rules automatically, so you don’t have to teach it because it learns patterns by analyzing massive amounts of text. That’s why the output sounds more like something a human might write.
NMT systems are also designed to improve continuously, so as the model is fed with new data, it can refine its internal representations and become more accurate. This ongoing learning process makes the model a future-proof solution because it will continue to evolve as languages change over time. Any new slang, shifts in cultural usage, and changes in professional vocabulary can be incorporated relatively easily.
Benefits of using NMT
Do you want:
- Faster translations,
- Better translation fluency,
- Better handling of idioms and context,
- Strong performance across many language pairs?
Neural Machine Translation can help with all of the above! NMT can maintain context, capture meaning more accurately, and produce output that reads quite naturally. Context-awareness helps the model resolve ambiguity and handle complex grammar, idioms, and long sentences more reliably. For most practical use cases, you get fewer errors, fewer edits, and your translations will feel closer to human-written content.
NMT models can also give you super-fast translations, as they can translate large volumes of text almost instantly. They can be fine-tuned with specialized data, even the general purpose models, so it’s very much possible to adapt the model to your domain or brand voice without redesigning the system.
Tips to get better translations
Ready to start using Neural Machine Translation systems for your localization? Here are a few ways you can get the most out of this.
Write clearly
The simplest thing you can do to get better translations is to write as clearly as possible. Neural models perform best when the meaning of a sentence is easy to interpret, so try to avoid ambiguous references, overly complex structures, or sentences with multiple possible interpretations. Small improvements in how you phrase things in the source language lead to better outputs.
Keep sentences manageable in length
Extremely long or overly dense sentences are still challenging to NMT systems. If your text has multiple ideas crammed into a single line, the system may struggle to maintain coherence or may drop important details. You can fix this by breaking long sentences into two or three shorter ones. This doesn’t mean oversimplifying the content, it just means separating ideas so the model can follow them more easily.
Use consistent terminology
If you want consistent translations, it helps to maintain consistent terminology in your original text. NMT systems notice patterns, so if you refer to a concept the same way every time, the translation will also stay consistent. Don’t switch between synonyms or mix formal and informal terms because you’re only going to confuse the model. Many companies use a glossary or termbase that defines which words to use for specific ideas to help with consistency.
Provide context whenever possible
NMT systems thrive on context, so feed them more information than just the raw sentence can lead to better results. If you’re using an API or a translation workflow that allows additional metadata, use it. If not, you can still help the model in a few ways: adding clarifying phrases before or after ambiguous sentences, specifying subjects clearly, including the surrounding text when translating.
Review and post-edit strategically
You can also get the best results by combining the model’s strengths with your own judgment. Targeted post-editing saves time and still gets you high-quality results. Focus on key areas where errors are more likely to occur. Over time, you’ll also start to recognize the common patterns in the model’s output, and you’ll learn to refine how you write, prepare, and review your content.
Fine-tune or customize
If you work with a specific industry, you could consider using a custom or fine-tuned model. Any type of domain-specific data can make the model far more accurate in your niche. Unfortunately, not every platform offers this option, but when available, it’s one of the most effective ways to improve translation performance.
Localize your content using NMT with POEditor
If you manage software, websites, or apps and need to translate many strings, POEditor is a tool that can get the job done. It’s a translation-management and localization platform that offers integrations with automatic machine translation engines.
You have the following NMT options to chose from if you wish to translate your content:
- Azure AI Translator
- DeepL Translate
- Google Translate
Because POEditor supports multiple translation approaches and translation-management features, you can mix them to get the best results in many contexts. Use an NMT engine or LLM-based AI to quickly generate a draft translation across many languages, and leverage translation memory or glossaries to ensure consistent terminology. And always review and do any necessary post-editing to ensure the quality of your translations!