Why use neural machine translation and where can you start?

For decades, scientists and engineers have been working hard to develop the full potential of neural machine translation (NMT). While NMT models like those used by Microsoft Translator have transformed how we communicate globally, the translation industry is now gravitating towards new advancements, like Small Language Models (SMLs) or Large Language Models (LLMs) fine-tuned specifically for translation tasks. For example, Google now positions its new Gemini-based Translation LLM as a solution for a wide range of translation use cases over its commonly-used NMT model, Google Translate.   

Despite these advancements, NMT still plays a role in translation today. Whether instantly translating road signs from a picture or interpreting business and legal documents, NMT has proven indispensable across numerous industries. Companies that leverage the power of machine translation and artificial intelligence (AI) save time, break language barriers, and reach international markets faster than their competitors that use traditional translation methods.

Here, we’ll define neural machine translation, how it works, and when to use it for your language translation needs to improve global reach.

What is neural machine translation?

Neural machine translation uses neural networks to provide accurate translations in various languages. Computer programs that employ NMT automatically translate text from one language to another, leveraging patterns learned from large datasets.

Not long ago, NMT was viewed by many as an inconsistent translation tool for translating important or high-visibility content, especially at scale. However, its ability to capture subtle linguistic nuances has drastically improved over the past decade. Now, it’s a go-to method for translating text, revolutionizing our approach to language translation and localization.

For example, AI translation software, such as Smartling’s neural machine translation hub, uses machine learning to select the best MT engine, ensuring accurate translation results in milliseconds. Smartling provides a central workspace to process multilingual content across various translation methods, workflows, and file formats. It’s an invaluable translation tool for international businesses that must communicate in different languages, producing high-quality translations you can trust.

How does neural machine translation work?

Neural machine translation uses artificial neural networks to mimic the human translation process at a much faster pace. These networks comprise layers of interconnected nodes that encode the source text, decode it into the target language, and employ an attention mechanism to ensure contextually accurate translations. NMT models learn from vast training datasets in each language.

What is a neural network?

A neural network is a computer system designed to mimic how the human brain processes and learns information. It’s made up of interconnected nodes, or “neurons,” that learn by passing information between nodes and adjusting connections based on training data to minimize prediction errors. This process teaches the network how to interpret and react to data, optimizing its performance for an assigned task, such as translating text.

What is deep learning translation?

Deep learning translation  is an advanced branch of AI that specifically utilizes deep neural networks. Unlike traditional or “shallow” NMT models that use just one or a few layers, deep learning uses many layers to analyze and learn from language data. This depth allows for a richer understanding of nuances, significantly improving translation accuracy and naturalness.

Different types of NMT models

There are two primary types of neural machine translation models, each with a unique approach to translating languages:

Transformer models

Transformer models process all words in a sentence at the same time. They use a system called self-attention, which weighs each word in reference to every other word in the sentence. This makes it easy for these models to understand relationships between words, even if they are far apart. After processing the words, they pass the information through feedforward layers to refine their understanding. The ability to process words in parallel makes transformers much faster and more powerful than older models.

Transformers can even translate in “zero-shot” scenarios, meaning they can translate between language pairs they haven’t been trained on by drawing from training data on each individual language. This makes them flexible and good at predicting translations across numerous language combinations.

RNN models

Recurrent neural networks (RNN) process words sequentially, meaning they look at one word at a time and pass information from previous words forward. This helps them understand patterns over time, but it also means they struggle with long sentences because they can “forget” earlier words. Since RNNs process information step by step, they are also much slower than transformers and can’t handle large datasets. RNN models are rarely used anymore as they have been replaced by transformers, which are far more accurate and efficient. 

When to use neural machine translation

Neural machine translation has evolved over the years to suit countless use cases. Here are some of its most common applications in business environments:

Streamlined workplace communication

NMT simplifies communication between employees and departments in global companies by translating emails, messages, and internal documents. Everyone stays on the same page, no matter where they’re from or what language they speak. It’s a great way to build a stronger, more connected team.

Real-time customer support

Global companies must handle customer inquiries from every market in which they operate. NMT enables real-time translation of customer inquiries and support responses, allowing companies to instantly assist customers in their preferred languages. International operations no longer require a multilingual support team.

Major translation projects

NMT can translate entire websites, user manuals, and other documents cheaper and faster than human translators. AI translation tools like Smartling’s Neural Machine Translation Hub streamline the process, providing high-quality translations in no time. Smartling’s NMT hub is ideal for website localization or updates to multilingual content repositories.

Translations of specialized content

Translations must uphold strict regulations in sensitive industries like healthcare or finance and preserve technical jargon. NMT systems can be trained on domain-specific data, easily translating complex or sensitive content while keeping the original meaning.

Advantages of neural machine translation

There are numerous benefits to integrating neural machine translation into your translation strategy.

• Faster time to publish: NMT engines can translate large amounts of text almost instantly, enabling quicker distribution of information across multiple languages. 
• Cost savings: Machine translation services are generally more affordable than human translation.
• Improved scalability: NMT can translate large volumes of text without a proportional cost increase. This scalability helps businesses of all sizes expand their global reach.
• Linguistic consistency: NMT uses consistent terms across all translated material thanks to translation memory. This uniformity strengthens a brand’s voice while maintaining clarity.
• Customizability: Training custom NMT engines with company-specific terminology produces higher-quality translations. Plus, NMT systems learn and improve over time, continually improving translation accuracy.
• Factual accuracy: NMT delivers highly accurate results with consistency. While fine-tuned LLMs are becoming more popular, they do introduce the possibility for hallucinations that can diminish accuracy without proper mitigation.
• Utility: NMT models are easy to understand, set up, and execute translations for a variety of use cases.

Best practices for incorporating NMT into your workflows

Incorporating neural machine translation can significantly improve translation efficiency and lower costs. These best practices will fine-tune your NMT process:

• Select a suitable machine translation engine: Not every NMT engine is ideal for every scenario. Evaluate options based on pricing, supported language pairs, customization features, and ease of integration into your existing systems. A thoughtful choice will align with your unique needs and budget.
• Leverage domain-optimized engines: If your industry relies on technical or scientific terminology, consider using an engine trained for your domain. If a pre-trained engine isn’t available, you may seek to train a custom one. You can update it over time as your needs evolve.
• Prepare your content with pre-editing: Structured, unambiguous text delivers the best translation results. Pre-editing can reduce errors and improve translation accuracy.
• Monitor and evaluate translation quality: Establish a feedback loop to regularly assess your NMT output. Automatically score your content to evaluate the overall quality of your translations. Then, select a sample of these results for human review to compare automated scores with linguistic standards. This method helps you to estimate how much post-editing a translated text needs, if any.

One centralized hub for smarter machine translation

You can’t rely on a one-size-fits-all solution to scale your localization efforts. Machine translation engines excel in specialized areas. Some are ideal for niche industries or domains, whereas others perform better with specific language pairs or content types. Limiting yourself to a single engine means missing out on the full potential of AI-powered translation.

With Smartling’s MT Auto Select, you don’t have to settle for one solution. Integrated within our Neural Machine Translation Hub, Auto Select gives you access to top engines like DeepL, Microsoft Translator, and Google Translate—all working together to deliver fast, accurate, and scalable results. By automatically selecting the best machine translation engine for your content and locale pair, Auto Select yields 350% higher-quality translations to improve your multilingual SEO and branded communication.

Today’s translation tech spans far beyond just NMT. The next generation of translation solutions, like Smartling’s AI Translation, leverage both NMT and LLMs to achieve even more efficient, fluent results.

Ready to take your localization strategy to the next level? Download our guide, “AI-Powered Localization: Achieving Quality, Cost, and Speed Objectives.” This resource will show you how to maximize the power of AI in your workflows.