Before google translate, machine translation was a dream. However, it’s just in the last decade that scientists have invented a way to make computers translate one language into another.
What is machine translation?
In the modern world of technology, machine translation has become increasingly popular. Machine translation (MT) is an automated translation technology that uses computer algorithms to translate text from one language to another. It is used to bridge language barriers and help people communicate with each other despite the language difference.
Until the early 2000s, it was a very complex mission for computers, and scientists were not able to come up with a method to make the process more applicable. Later, developers trained computers with statistical databases of languages to translate text. The training involved a lot of manual labor, and each additional language required starting over.
An experimental team at Google tested neural learning models and artificial intelligence (AI) for training machine translation engines in 2016. The small team’s methodology proved to be far more efficient and effective across many languages than Google’s main statistical machine translation engine when it was tested against it. Moreover, it ‘learned’ as it was used, generating constant quality improvements.
Automated vs machine translation: What’s the difference?
Though it might get mixed between automated and machine translation, there is a difference between the two.
Automated translation refers to the process of building an automated tool in a traditional computer-assisted, such as a computer-assisted translation tool, known as (CAT Tool) or modern translation management system (TMS) to minimize the amount of human intervention in translation-related manual or repetitive tasks.
The automated translation may be used in inserting commonly used text such as legal disclaimers into documents from a database like a content management system (CMS). Also, it can automate the machine translation of the text as a stage in the localization workflow.
What types of machine translation are there?
Basically, there are three main types of machine translation, which are:
1- Rule-based machine translation
It is possible to say that this is the least used type of MT, as it has a lot of disadvantages compared to the human current needs. The downsides include that it requires significant post-editing, that it must be added languages manually, and that it has a low-quality in general. It has some uses in situations where meaning must be understood quickly.
2- Statistical machine translation
This type of MT has a medium understanding of the nature of language. It builds a static and structured relationship between words, phrases and sentences in the text in order to convert them into the target language. However, it’s not the best model of machine translation as it shares some rule-based MT issues.
3- Neural machine translation
If we want to describe it in a way that we all can understand, it is similar to the neural networks in the human brain, well, even if we are not neurologists we still can understand it. However, neural MT is based on the use of AI in understanding both the original and target languages and their linguistic conditions. It is easier to use, gives more accurate results and is faster.
Which machine translation type should I use?
It is an important question to ask: how to determine which kind of machine translation should you use? To answer this question, first, you need to answer these questions:
- What is your budget? The cost of neural machine translation is higher than statistical machine translation, but quality improvement is worth any cost difference. Hence, many systems are opting for neural machine translation over statistical machine translation.
- What is your industry? Some industries require more efficient translation and accurate results, such as political research and conferences, as a result, you need to use a very developed kind.
- What language do you need? When there are similar grammar and syntax rules among Latin-based languages, statistical MT can often suffice.
- How much data do you have? As neural MT is based on artificial intelligence, it requires a huge amount of previously translated data to function and give results.
- Internal vs customer-facing content: It takes a combination of machine translation and experienced human translators to produce customer-facing content that reflects brand quality, such as marketing and sales materials. In cases of limited time and cost, basic machine translation may be sufficient for employee communication or internal documentation.
Machine translation vs human translation
After machines were introduced to the game, humans stopped doing the whole job by themselves, however, they still until our living day do a part of the job.
Basically, one of the core advantages of machine translation as an alternative to translation from scratch is machine translation post-editing (MTPE), which is the editing of MT-ed content by a human linguist. Businesses and language service providers and translators increasingly recognize this concept.
Machines may understand the meaning of “apple” in Arabic, French, German & all other languages that are discovered in the world. But until now, this is their maximum, this is the most they can do. On the other hand, they can’t translate emotions, thoughts, empathy and ideology.
Let’s take a real example; when translating “LesMisérables”, the original historian French novel written by Victor Hugo, it is essential that the Italian reader would sense the misery and sadness in the Italian words as a part of transferring the emotions in the translation. The mere translation of words isn’t enough, and this is what MT is incapable of, yet, humans are.
On the other hand, in terms of translating more technical pieces of content, machine translation can be more useful as it is faster, more efficient and save effort.
What are some major machine translation providers?
Giant techs like Google & Microsoft depend on neural machine translation in their applications and technology as it allows for both more nuanced translation and constant adding language pairs.
Here are the most known apps from translation providers:
Google Translate
We know for sure that each phone has Google Translate installed. It is considered the most known leading machine translation engine. With its neural language processing-based approach, Google Translate was the first machine translation engine to learn from repeated use. It has been developed over the years to become more accurate, logical and right.
Amazon Translate
The Amazon Translate platform is based on neural networks and tightly integrated with Amazon Web Services (AWS). There is some evidence that Amazon Translate is more accurate when it comes to certain languages, notably Chinese, but when making comparisons it is important to remember that all of these engines are continually learning and improving.
Microsoft Translator
Microsoft Translator, another cloud-based neural engine, provides instant access to translation capabilities within MS Office and other Microsoft products.
What are the advantages of machine translation?
When neural learning was introduced to MT, its quality varied greatly, sometimes even reaching humorous levels or being unreadable. Before the introduction of neural learning, MT was still very much a beta product. All of that has changed dramatically with the advent of modern machine translation tools, which have become increasingly indispensable in translation.
– Speed and volume
As more content is translated, MT continues to improve, translating millions of words almost instantaneously. MT has the ability to handle large volumes of content at speed, as well as work with content management systems to organize and tag it. With this method, the content can be translated into multiple languages while maintaining context and organization.
– Large language selection
Major providers offer translation services in 50-100 languages at a time, making it possible for global product launches and documentation updates to be done simultaneously in several languages.
– Reduced costs and faster turnaround
In addition to its high speed and ability to select from existing language pairs covering dozens of combinations, MT can reduce translation costs and time, even while human translators still post-edit the work. MT essentially provides basic but useful translations after the initial heavy lifting is done. These basic versions are then refined by a human translator to ensure proper localization and reflect the original intent.
Maybe we can’t recall the last time we did a whole translation using just our human brain and talent, it’s not because it’s impossible, as humans can always do the entire job, but because there are ways to make it easier and faster, and that is by using machine translation. However, 100% dependence on MT will never be the solution, we will always need our human touch.
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