Machine translation has come a long way, from scholars translating Greek texts over months to your phone translating virtually anything in seconds.
The promise of machine translation technology is compelling. Reach your audience anywhere in the world in their native language without spending a fortune on human translators. It’s fast, adaptable, and highly scalable.
In this article, we break down what machine translation really is, how it works, and where it shines (and stumbles).
What is machine translation?
Machine translation (MT) is a computerized process of translating text from one language to another. A machine translation system evaluates grammatical patterns, uses learning algorithms, and refers to previous translations for translating any text.
👀 Machine translation ≠ AI-powered translation
Machine translation analyzes patterns in a large dataset and generates translations by connecting words or phrases that frequently appear together across languages.
Generative AI, on the other hand, is more advanced than MT. These tools use transfer learning from other tasks and existing training data to produce clear and accurate translations.
Learn more about these differences here.
Learn more about what AI translation is here.
Benefits of machine translation
Machine translation can do wonders for your translation projects, especially if you have a tight budget and timeline. Here’s how:
- Speed and scalability: Machine translation technology makes it easy to process a large volume of data in seconds. Imagine translating documents, websites, and content assets, all in a few minutes. This speed and scale eliminates many bottlenecks for businesses looking to expand quickly.
- Cost efficiency: When compared to human translation, MT can significantly reduce the costs involved. That means even teams with limited budgets can produce multilingual content. Besides, it’s particularly useful for high-volume, lower-stakes content.
- Translation consistency: Working with multiple human translators means you’re bound to see some discrepancies in the terminology used. With modern machine translation systems, it’s easier to maintain consistency in the terms used across all projects.
- Continuous learning: Machine translation systems constantly learn and improve. This is possible through new training data and user feedback. This progressively enhances the quality of translations produced.
Limitations of machine translation
It’s not always the best idea to rely on machine learning translation. Here are some challenges you might face with this technology:
- Inconsistent quality: Translation quality can vary a lot between language pairs. Popular pairs (like English-Spanish) would perform better since more training data is available. On the flip side, you’ll struggle with the quality of translation in low-resource language pairs.
- Cultural context: Another major downside of using MT technology is its inability to include cultural references, humor, idioms, and contextual nuance. Only human translations can instinctively understand and add this refined touch to your translation projects.
- Domain-specific terms: General systems are prone to mistranslating specialized terminology for fields like law, medicine, finance, and more. You have to thoroughly edit and fact-check translations to prevent any misinterpretations.
- Confidentiality concerns: Many open-source MT software store translated content and train on this data. This puts sensitive business or personal information at risk. You can’t use free tools to translate confidential content.
📚 Further reading: Can LLM translate text accurately?
Before we learn the mechanism behind machine translation, let’s trace the timeline of how it emerged and evolved over the years.
A brief history of machine translation
Believe it or not, the idea of machine translation has a history long before computers.
Here’s a brief history of events that turned machine translation from a small practice to present-day technology.
| Period | Key Figure/Event | Contribution |
| 9th century | Al-Kindi | Oversaw the translation of ancient Greek scientific and philosophical texts into Arabic. Pioneered early translation methods using cryptanalysis, frequency analysis, probabilities, and statistics. |
| 1629 | René Descartes | Proposed a universal language of symbols to express similar ideas across different languages |
| 1930s | Georges Artsrouni | Filed patents in France for one of the first translation machines |
| 1930s | Petr Petrovich Troyanskii | Independently filed patents in Moscow for a translation machine |
| 1949 | Warren Weaver | Presented the first formal proposals for computer-based machine language translation |
| 1954 | Georgetown-IBM Experiment | First public demonstration of machine translation (Russian to English) |
| 1984 | SYSTRAN | Early commercial MT system adopted by the European Commission |
| 2006 | Google Translate | Launch of statistical MT service supporting multiple languages |
| 2014 | Neural Networks | First applications of neural networks to machine translation |
| 2016 | Google Neural Machine Translation | Google implements neural MT, dramatically improving the quality |
| 2017 | Transformer Architecture | Introduction of the Transformer model revolutionizes NMT |
| 2018-present | Multilingual Models | Development of massive multilingual models capable of translating between hundreds of language pairs |
How does machine translation work?
When humans translate a text, they use their knowledge of the source language to understand the intended meaning behind the words. Then, they apply their knowledge of the target language to convey this meaning in a new language as closely as possible.
A computer is much more methodical by nature.
Machine translation follows a predefined set of rules or steps:
- Filter and organize the source text in a clean document
- Reference a database of similar passages translated from the source to the target language
- Spot patterns to understand which words to select and how phrases are structured
📚Further reading: Learn how AI translation works from our easy-to-understand, jargon-free guide.
Translating large amounts of text by hand takes much time and effort. By automating the process, machine translation aims to produce more accurate results in less time than a human can.
Let’s look at some best practices when working with machine learning translation.
Prepare source content for optimal results
Before running your content through the machine translation process, organize your source material. You have to edit and enhance this text by:
- Using direct language and avoiding complex sentences
- Eliminating idioms, ambiguities, and regional references
- Breaking ideas into small segments with clear context
This prep work can improve the output quality. Plus, it saves you the hassle of extensive post-editing work.
Create a post-editing workflow
Editing machine-translated text allows you to create a better output without compromising speed.
A systematic MT post-editing workflow involves a human translator reviewing and refining the translated text. You can determine different levels of editing based on the quality required, like light editing for internal documents and in-depth review for customer-facing communication.
Maintain terminology databases
Building a terminology database is a surefire way to improve the quality of machine-translation output. Integrate this database(s) to train your MT system to use specific industry or company terms instead of generic vocabulary.
You should also have processes to regularly update these databases and create new ones for the languages added in the future.
Test and evaluate output regularly
Machine translation tools don’t work well with a set-it-and-forget-it approach. You need to continuously evaluate the quality of output and fix errors.
Use methods like BLEU scoring to assess the fluency and accuracy of translated text. You can also collect user feedback to understand where your system is currently lacking and how to improve the quality.
Remember to track progress by benchmarking results against previous outputs.
Different approaches to machine translation
Modern machine translation technology draws heavily on the past. Many approaches are extensions of how the technology worked in its earlier versions.
Let’s discover the four main approaches to machine language translation.
Rule-based machine translation
Warren Weaver, one of the earliest pioneers of MT technology, proposed the rule-based machine translation (RBMT) system in the 1950s. This approach follows a predefined set of rules.
For about 30 years, that was the only way computers could translate text.
This approach is often called the classic method because it relies on linguistic rules and dictionaries. Human language experts must manually create, maintain, and update these rules constantly.
Its manual nature makes the approach time-consuming. It also struggles with ambiguous terms, leading to lower-quality translations with stilted or “machine-like” output.
Statistical machine translation
Computing power increased dramatically in the 1980s, and prices started dropping. This led to a new approach called statistical machine translation (SMT). It uses statistical models and large volumes of bilingual data to translate text from one language to another.
An SMT system learns to translate by analyzing the statistical relationships between original texts and existing translations of those texts. Its output for a new translation is then put through a language model to give the translated language a more natural flow.
A big drawback of this approach is the cost. Creating the parallel data and training the system is laborious and time-consuming. It’s also less accurate when working with language pairs with differences in word order, such as English to Japanese.
Neural machine translation
The next technological advance led to the most sophisticated of the three, where artificial intelligence (AI) appears.
Where previous models were phrase-based systems, the neural machine translation (NMT) model considers entire sentences at each step. This natural language processing allows tools to understand the context and deliver far more accurate translations.
The most remarkable feature of NMT is its ability to learn and adapt to new contexts with each piece of text it translates. This makes NMT an ideal solution for quickly, accurately, and flexibly translating lots of text.
📚Further reading: Want to learn more about different types of machine translation? Read our in-depth guide to discover when it makes sense to use one over the other.
Computer-assisted translation tools
Since the technology still needed improvement, computer-assisted translation (CAT) tools emerged. The idea was to let technology help translators with some parts of the process.
CAT tools break up source text into smaller, more manageable segments (often sentences). Next, human translators work on individual segments, referring to the original text when necessary. Then, the tool stitches the segments back together and spits out the translated text for review.
Most CAT tools include glossaries, translation memories, and other advanced features to further streamline the process.
5 machine translation tools to explore
Now that we’ve understood how the machine translation process works, let’s cover five tools that generate the translated text.
1. Google Translate
Google Translate supports over 133 languages, making it one of the most widely used platforms worldwide. It uses Neural Machine Translation (NMT) based on Google’s Transformer architecture.
The platform is available as a web interface, mobile app, and through API access. It uses automatic translation to provide instant translations for any webpage.
The tool is also popular for its real-time conversation translation capability. You can also translate text into images using camera translation. Plus, translate any website using its browser extensions.
2. DeepL
DeepL uses large language models to translate complex content, making it a preferred MT system for European languages. This platform is built on Linguee’s translation database.
DeepL currently supports 29 languages. It’s proficient in handling context-specific translations with advanced controls to adjust the tone between formal and informal. It’s available as a desktop app and through API access.
The platform also lets you create a glossary for terminology management.
3. Microsoft Translator
Microsoft Translator is deeply integrated across Microsoft’s ecosystem of products, including Office, Bing, Skype, and Edge. It supports over 100 languages and uses neural network technology.
One of its standout features is the ability to translate multi-person conversations in real-time. You can also translate videos, images, and speech.
Besides personal and educational use cases, MS Translator offers a business plan for enterprise translation needs.
4. Amazon Translate
Amazon Translate is part of AWS’s suite of machine learning services, integrated with other Amazon services. It’s primarily made for business applications.
The platform supports over 75 languages with real-time translation options. It uses natural language processing to convert text from one language to another. You can translate content in batches to cover a large volume at a time. The system also has a self-learning mechanism to improve the output and make progress.
5. Lokalise
Lokalise AI is purpose-built for businesses to create machine-generated translations with 80% translation accuracy.
You can translate different types of assets and content across your website, mobile app, software product, and more.
Lokalise AI uses the best MT engine (like ChatGPT, DeepL, and more) after evaluating your specific needs. The platform also works with a translation memory to reuse translated text from the past without the constant back-and-forth.
What’s unique about Lokalise is the ability to build your own style guide and glossary. You can tailor all output to your brand’s style and achieve more consistency.
What can machine translation do for your business?
Expanding your business into new markets means you have to localize your website, social media, pitch decks, and a lot more.
Machine translation makes it easy to translate any asset into any language within minutes. Whether it’s your internal comms or customer-facing content, MT tech can streamline and fast-track translations for your business.
Here are a few use cases to demonstrate that:
Internal communication
For global teams, writing all company emails and interoffice communications in English can lead to misunderstandings—even where it’s a native language. It’s the same with other languages.
The French spoken in West Africa is different from the French spoken in France, for example. And the complexities ramp up very quickly the further afield you go.
That’s where machine language translation can help. It can translate your messages (or voice) on the fly to avoid disrupting the conversation. It can translate replies back into your language almost instantly. Speed is essential when communicating urgent messages globally, and you need rapid responses.
Other uses of machine translation
There are many other places where machine translation can help you, each with its own needs in terms of the balance between cost, speed, and accuracy:
- Conference calls: Speed is critical because slow translation disrupts conversations. It’s up to you whether cost or accuracy is the second most important.
- Product instructions: Speed shouldn’t be a problem here, allowing you to find the right balance between cost and speed.
- Marketing material: This is another use case in which time is usually a minor issue. You can focus on either cost or accuracy.
- Websites: Speed is the most critical factor here, followed by accuracy. Because the pages are live, you can quickly fix typos and other issues. The speed does come at a higher cost.
Can machine translation replace humans?
While machine translation has evolved with technology, it remains an imperfect substitute for human translation.
MT excels at speed and scale. But it falls short when you want to add more nuance to your translated text. The technology lacks cultural intelligence. As a result, it struggles to incorporate cultural references and contextual subtleties.
That’s where you need human oversight to make post-edits and refine the output. Human translators can edit the machine-generated text to make it more appropriate and accurate.
That’s why the future likely belongs to a hybrid human-machine model to achieve higher translation accuracy and scalability.
Let Lokalise help you grow your business globally
Machine translation is unlikely to completely replace humans for some time, if ever. But it can definitely speed up and improve the translation process.
You don’t have to learn a new language to reach new audiences. Today’s AI-powered tools are breaking down borders. With the proper localization and translation tools to streamline and simplify your workflow, you can reach new audiences without breaking a sweat.
Don’t take our word for it, though. Start your free trial today with Lokalise and see for yourself.
FAQ
What is the difference between machine translation and language translation?
Machine translation usually produces acceptable translations for uncomplicated content, but it may fail to translate specialized, technical, or complex material. Human translation is strongest in these cases and can offer higher accuracy, cultural sensitivity, and more natural-sounding results.
What is the most accurate machine translation technology?
It’s generally accepted that neural machine translation is currently the most accurate, flexible, and fluent form of machine translation technology.
Is machine translation getting better?
Machine translation has been improving since its inception. Regular advancements in AI technology and the use of style guides and glossaries have enabled it to continue improving.
Can machine translation replace human translation?
In some applications, yes. Today, highly accurate translations requiring few post-edits are possible, but they’re still imperfect. This is especially true for complex texts that don’t use formulaic language.
How does text translation work?
How do you eat an elephant? One bite at a time. It’s the same with translation. Human and machine translators work on text in bite-sized chunks of 5-10 words. They’ll constantly refer to dictionaries, glossaries, and other sources to convert the source text into the target language.
Is ChatGPT a translation tool?
The short answer is yes. But it’s not something you should trust to translate important text because the results are not as accurate or precise as a specialized translation tool or a professional translator would be.
