What Is Machine Translation? Overview of the Best Machine Translators
What is machine translation?
Imagine your content is available in multiple languages.
Now imagine it’s instantly available in multiple languages at zero cost to you.
That’s what machine translation software can do for your business.
The best part? You don’t have to just imagine it. The best machine translators are freely available and can instantly localize as much content as you need.
Keep reading to discover:
Pro tip: To manage a complex translation project, use a professional localization suite with built-in machine translation functionality. With it, you can save time and achieve the highest quality of translations. Learn more.
What Is Machine Translation?
Machine translation is a language translation technology that automatically translates content into the target language. All you need to do is feed the source text into it.
Machine translation technology uses linguistic rules, statistical data, and text corpora to analyze the source text and predict the best translation.
4 Types of Machine Translation
There are three main machine translation systems and a hybrid one combining some or all of them.
Rule-Based Machine Translation (RBMT)
Rule-Based Machine Translation (RBMT) is the most basic machine translation system that uses linguistic rules to translate texts from one language to another.
The rules include the grammar, syntax, and semantics of both the source and target languages.
The RBMT technology was invented in the 1960s and is still occasionally used for rough translation of the source content. However, its application is severely limited because it relies heavily on human input at the editing stage.
Statistical Machine Translation (SMT)
Statistical Machine Translation (SMT) is a type of machine translation system that learns how to translate text by using statistical models derived from existing translations.
Instead of relying on explicit linguistic rules, SMT uses large collections of bilingual texts known as parallel corpora to recognize patterns and the probability of their occurrence.
SMT creates more natural-sounding translations and handles complex language phenomena better than RBMT. However, it still struggles with ambiguous or idiomatic language as well as some language pairs.
Neural Machine Translation (NMT)
Neural Machine Translation (NMT) is a machine translation technology that leverages artificial neural networks.
NMT can produce translations that are more accurate and natural than RBMT and SMT. This is because NMT can handle complex language phenomena, such as idiomatic expressions and wordplay, more effectively.
NMT is also more adaptable as it can be trained on large amounts of data without requiring expert linguistic knowledge or manual rule creation. That’s why NMT is the dominant kind of machine translation.
Hybrid Machine Translation (HMT)
Hybrid machine translation (HMT) uses a combination of the three other approaches to improve the quality of translations.
HMT is used in the following ways:
Multi-engine approach: Several translation systems run simultaneously and their output is then merged into the final translation.
Statistical rule generation: Statistical machine translation helps create the rules that are used for rule-based translation.
Multi-pass approach: Starts with rule-based translation, followed by statistical machine translation, and finally NST. Each round of translation refines the quality of the output.
Confidence-based method: The first round of translation is ranked on a confidence score to determine whether the second round is necessary.
Advantages of Machine Translation
You would be hard-pressed to find a localization professional who does not use machine translation to some extent. The widespread adoption of machine translation can be attributed to its multiple benefits:
High speed: Machine translation tools process large volumes of text in very little time.
Low cost: Machine translation reduces translator workload and cost by providing them with a rough translation of the content. As a result, their role shifts from translation to editing.
Multiple languages: The biggest players in the machine translation business allow you to translate your content in up to 200 languages.
Integration with TMS: Translation management systems (TMS) like Centus integrate machine translation into their workflows, thereby simplifying the translation process for human translators.
Disadvantages of Machine Translation
While there are some clear advantages of embedding MT in your workflows, the technology comes with a few limitations:
Low accuracy: Machine-generated translations are less accurate and natural-sounding than those produced by humans.
Lack of domain-specific knowledge: MT systems underperform with specialized or technical content because of the lack of parallel texts to learn from.
Lack of context awareness: Human translators have a clear advantage over MT as they can interpret the intended meaning better.
Need for post-editing: MT requires post-editing by a human translator to correct errors and improve the translation quality.
Use Cases of Machine Translation
The suitability of machine translation depends on the content type and its importance. Generally, MT can be used to translate well-written and clearly-structured content, like documentation.
Some of the common MT use cases include:
Internal communication: Multinational companies can use machine translation services to remove the language barriers between their employees around the world. This can be done by individual employees as well as whole teams to prepare versions of internal documents in their own languages.
External communication: MT is equally effective at translating large volumes of content for external stakeholders. This could be anything from documents for business partners to product descriptions or reviews for their e-commerce websites.
Short-lived content: MT is used to translate reviews, social media comments, or customer support call transcripts. The translated content is then analyzed to extract insights that could inform product development, marketing or customer support decisions.
Online customer service: Machine translation is often integrated into chatbots, allowing companies to deliver quality customer support around the globe.
Software localization: MT is widely used for localizing UI, in-app communication, product manuals, guides, and marketing collaterals.
Key Features of the Best Machine Translation Tool
Before we explore some of the best machine translators currently available, let’s look at a few things that you should be paying attention to when choosing your tool:
Wide language selection: Make sure your MT engine covers the languages you currently need. Opt for tools with wide language support, because you might want to target new markets in the future.
High translation accuracy: Although most MT tools support multiple languages, their translation quality varies widely. Google Translate, for example, performs well with most spoken languages but gives way to small MT tools for the least spoken languages.
Domain-specific translations: Some MT models deliver better translations for certain subject domains. Choose the one that offers the best results for your niche.
Custom terminology support: The best MT tools allow you to train the models with your glossaries or use them to adjust the translation output.
API support and tool integrations: Look for a computer-assisted translation (CAT) tool or translation management system that can be integrated into your website or software. The integration will allow you to streamline the translation workflows.
5 Best Machine Translators
The table below shows a quick overview of the best machine translators on the market.
Now let’s examine each machine translation tool to see what makes them great and how they can benefit your business.
Google Cloud Translation
Google is usually associated with Translate, the go-to machine translation tool for millions of people all over the world. The quality of the translation may vary, making the general public skeptical about the reliability of machine translation tools.
However, not everyone knows that Google also has one of the best-performing MT tools that is not freely available to the public. Meet Google Cloud Translation – a commercial MT tool that leverages natural language processing (NLP) and AI.
The tool features API, glossaries, custom models, and batch translations. It supports over 100 language pairs, the translation quality of which is the highest on the market according to the State of Machine Translation report.
Google Cloud translation is available in three editions, the most affordable of which costs $20 per million characters. Media translation is priced separately, at $0.068 per minute.
DeepL is the second-best-performing MT engine, only marginally trailing behind Google.
Developed by the German company Linguee in 2017, DeepL now supports 31 languages. The tool is powered by the company’s proprietary technology that leverages convolutional neural networks (CNNs). It has been trained using the company’s previous product Linguee, which is a translation service powered by a database of bilingual texts.
DeepL Pro users assess its translations as more natural sounding than those of other MT engines. The quality of Deepl Pro translation is also rated the highest in a study commissioned by the company.
The tool features API support allowing integration with the most popular translation management systems. It also has custom glossaries and a built-in dictionary. What sets DeepL apart from Google Cloud Translation is the ability to choose a formal or informal tone for translations.
DeepL Pro is available on a subscription basis. For $28.74/month, one gets access to unlimited translation and 2000 glossaries with 5000 entries each. Custom pricing is available for Enterprise users.
Like the other MT models, Amazon Translate uses neural translation to deliver its translation. The tool is available in 75 languages and can be integrated with the company’s services such as Amazon Comprehend, Amazon Transcribe, and Amazon S3.
Thanks to API support, you can use Amazon Translate for the website or app localization. To enhance the quality of translation, you can leverage the tool’s tone-of-voice feature.
Amazon Translate also allows you to customize the output of the translations with bespoke terminology and jargon so that it fits your branding and product. Finally, you can use Amazon’s MT for qualitative analysis of written content, like customer feedback.
Amazon Translate is available for $15 per million characters for Standard Translation and $60 per million characters for Active Custom Translation. That’s affordable even without the generous character allowance within the free tier – 2 million characters per year.
Just like Google, Microsoft has a freely-available translation service that is integrated into Bing and Skype. However, the company also offers Microsoft Translator for Business.
Microsoft Translator is based on neural machine translation and statistical machine translation, making its output incredibly accurate. Currently, the tool supports 112 languages.
With the API you can integrate Microsoft Translator into your products and use it for customer support, localization, or business research. Just like the other translation solutions, it allows you to adjust the output with custom glossaries.
Microsoft Translate also features transliteration and automatic language detection.
Microsoft charges $10 per million characters of standard translation, $15 for document translation, and $40 for custom translation. The company’s pricing strategy favors bigger businesses because it requires substantial commitment within Azure.
ModernMT is a translation tool that rivals the biggest players on the market while remaining more affordable. The tool is based on instance-based adaptive technology allowing it to adjust translation to the required subject domain without a lengthy learning process. Currently, ModernMT supports 88 languages.
What distinguishes ModernMT from its competitors is document-level translation. This means that the tool analyzes the whole text rather than individual sentences. In practice, context awareness leads to more nuanced translations.
ModernMT also learns from its users in real time, registering their preferences. Finally, ModernMT features API and CAT tools integrations, making it particularly useful for enterprises.
ModernMT is available as a plugin for translators at $25 per month.
Enterprises can use it to translate a million characters for $8 with the Batch edition, $15 with the Real-time edition, or $50 with the Human-in-the-loop edition.
Honorable Mention: ChatGPT
ChatGPT increasingly gains traction as a translation tool. Being a natural language processing tool, ChatGPT can produce translations on par with those by Google Translate. The tool supports 95 languages and keeps adding new ones incredibly fast.
By providing ChatGPT with a detailed prompt, you can ensure its contextual awareness, thereby increasing the quality of translation.
ChatGPT can also adjust its output to the type of text you need to translate. Finally, you can request the required tone and voice of translation in the prompt.
Just like purpose-built MT tools, ChatGPT features API, allowing seamless integration with apps and websites.
The commercial version of ChatGPT is available for $20 per month.
Pro tip: Rather than using a standalone translation tool, try a translation management solution - Centus. The solution has an in-built MT tool, translation memory, QA checks, API, and multiple features to streamline your translation project. Learn more.
Machine translation has some clear benefits, like increased speed and reduced translation costs. It’s mostly suitable for clearly structured texts, and less so for niche or creative content.
When it comes to choosing the best machine translators, there are several robust products out there. Although Google and DeepL currently lead the way, other companies like Amazon and Microsoft are not far behind.
What is the history of machine translation?
The first successful experiments with machine translation were carried out by researchers at Georgetown University in 1954.
In the 1960s, the US Department of Defence invested in machine translation, thereby facilitating the development of the technology.
In the 1970s, machine translation became commercially available in the US, Canada, Europe, and Japan. However, due to the low availability of microcomputers, only enterprises could afford it.
The 1990-2000s saw the democratization of machine translation. In 2016, Google became the first company to start using neural learning models and (AI) to train translation engines.
Is human translation better than machine translation?
Current machine translation technology is not able to fully understand the intention, style, or communicative purpose of the text. As a result, human translators are still better at translating creative or ambiguous texts.
Machine translation can be used effectively for translating low-impact or technical texts.
What’s the difference between automated translation and machine translation?
Automated translation is CAT tool automation for repetitive translation-related tasks. For example, it may automatically insert translation units into your texts whenever it comes across a previously-translated segment.
Is machine translation the same as translation memory?
Machine translation and translation memory are two different kinds of translation technology.
Translation memory is a tool that stores previously translated language units and suggests them to translators.
Machine translation, on the other hand, translates on its own without any human input.