Nearly everyone has heard of machine translation. Google Translate is an important and frequently used tool for many, and machine translations can also often be found on social media platforms. Almost every widely used social media platform gives the user the opportunity to see posts machine-translated into their own language. Machine translations can be found in many places, and the user may not always even realise that a translation is produced by a machine.
Machine translation methods
Although machine translation hasn’t been used extensively until quite recently, it has been an objective of data processing since the 1940s. The method of producing machine translations has changed a number of times over the years, sometimes basing the process on certain rules, at other times on statistics, and so on. These approaches, which are still partly used, had their advantages but have since become quite marginal. The biggest public progress in the field of machine translation in recent years was made in 2014–2015 when the first scientific article on neural machine translations was published and neural networks were used for the first time in a machine translation competition. At the moment, the market is completely dominated by neural machine translations.
Neural machine translations, often abbreviated as NMT, are the most important AI or, more accurately, machine learning application in the language industry. Neural networks calculate vectors for words that also take into account the context in which the word is used. As a result, neural networks very rarely produce grammatically incorrect text. There are some mistakes, of course, but they are primarily not grammatical and relate especially to semantics and terminology.
Today’s machine translation tools produce high-quality text, but they cannot replace human translation expertise and understanding of the broader context. Instead, they complement human competence and provide a basis for a number of new services.
Where to use machine translation
According to a Finnish proverb, fire is a good farmhand, but a bad master. The same applies to machine translation. Machine translation can be used to provide certain services that would not be possible without it. However, trying to use machine translation tools to produce all the translations you need may end up with you shooting yourself in the foot.
Let me now highlight a couple of applications that are ideal for using machine translation. (These are not the only possibilities.)
The support sites of web services can grow quite large over time or be very extensive from the start. New companies in particular rarely have an unlimited budget for localising their support site, and even large companies do not want to unnecessarily spend huge amounts in this area.
Machine translation is an invaluable tool for localising customer support websites. In principle, it provides almost instant translations into numerous languages at a very low cost. I say “in principle” because you have to remember the limitations of machine translation. The use of machine translation alone does not guarantee a flawless end result and, in the worst case scenario, the user may receive completely inaccurate information and misuse the product or service. Where can you find good guidelines for using machine translation? Perhaps the most important piece of advice is to know your product and be aware of its special requirements. In other words, if you are manufacturing, say, circular saws, do not rely on machine translation for your support site if you want to make sure that your customers don’t cut off their fingers. If your product is not directly hazardous if misused, you can be more flexible.
Even on support sites, it is not advisable to use the same processes for all content and languages. Through analytics, you’ll probably know which articles are used the most and which issues lead to the most support requests. It is probably a good idea to translate them more carefully than pages that are used very rarely.
The best toolbox for localising support sites contains various tools: translators, translation memories and machine translation tools – not to mention glossaries and other resources. Translation memories are used to teach the machine translation tool essential content and to achieve the highest possible quality in the raw translation. Translators edit the most important pages – sometimes all of them – to ensure perfect translation quality. Your language service provider will help you manage this complex package.
By the way, here’s an important but often forgotten tip: if you offer your users a “raw” machine translation, let them know. They will be more likely to forgive minor mistakes and they can check any ambiguous parts against the original language version, even if they are not fluent with it. If a user mistakes machine-translated materials for a human translation, they may become annoyed and consider the translator and, therefore, the service provider incompetent.
Internal materials do not always need to be translated perfectly. The translation should simply be very close to the original text so that a reader who is not proficient in the original language can understand its content. These texts include, for example, invitations to tender or comments from external users on various services. Such translations may have such a short lifespan and a very limited readership. As internal users, readers can also be very forgiving about small mistakes.
When internal materials are needed quickly, people can be easily tempted to use free online services where text can be copied and pasted. This is often a workable solution, but it has some obvious shortcomings: document formatting is lost, the results are not based on previous translations, and data protection concerning your information is at risk.
Fortunately, you can pick the best parts of each approach. Document-based machine translation considers the context, and the process can take advantage of the organisation’s approved materials through translation memories and adaptive machine translation tools.
An automated and fast solution does not have to be the simplest solution. If necessary, this process can also be integrated with a certain degree of a human touch, depending on the subject matter and purpose of the text.
Benefits of a great machine translation partner for your organisation
There are many different ways and purposes of using machine translations. Therefore, the delivery method of machine translation must also be flexible. Through API, the customer can receive machine translations directly to almost any system and, through various integrations, there are ready-made ways to deliver texts across multiple systems, such as content management systems and online stores. An ordering system based on entire documents is an easy way to make machine translations available to even more occasional users in your organisation.
The quality of machine translation is neither a random factor nor a “standard” that could not be influenced. A knowledgeable machine translation partner makes use of the customer’s text resources to improve the results, instead of simply offering what any customer would receive in any case.
A great machine translation partner also offers the most important building block: language expertise. Together with the customer, the machine translation partner is able to find the most suitable combination of machine power and human expertise.