When Translation Technology Really Helps and When it Doesn’t

Ciklopea 3 years ago 5 min read

Machine translation. Artificial intelligence. Neural language processing. Predictive modelling. These fancy terms have been flashing everywhere over the past several years to the point when it seems that all your localization problems can be solved with a double click. As of 2021, they can’t. In this article, we will try and explain the exact role of technology in the translation/localization process in the simplest possible terms and help you understand what it really can and cannot do for you.

Sophisticated as it is, technology is still “only” a tool

Induction hobs are a great invention. They are sleek, energy-efficient, fast, easy to use, safe, smart and everything in between. However, they cannot make you a star chef if hard boiled eggs mark the upper limit of your culinary skills. But if you happen to be something of an expert cook, there is no doubt that they will make your cooking faster, easier and more convenient.

This is basically the story of every technology.

Technology is a tool that helps us do more actual work with less effort, but its function is not to replace our knowledge, skills and dedication. The concept of doing more actual work with less effort naturally includes delegating repetitive and time-consuming tasks to the machines and this is very useful with, for example, various calculations and automated operations.

Most of the time, though, the high-level intellectual tasks such as content development and translation are time-consuming, but they are not repetitive and cannot be fully delegated to machines any more than, for example, flying an airplane.

One of the oldest examples of task delegating in translation is spellchecking. Of course, it is far more elegant, easier and faster to hit that F7 key and let your word processor automatically check the spelling errors than to sift through the document searching for errors for hours using your eyes only.

However, the spellchecker will actually compare the text against its database and report the strings that it cannot recognize as errors. Will all of them be actual errors? No. You will still need to check the spellchecker’s results.

In other words, technology requires human supervision regardless of its level of sophistication.

Learn More about the translation technologies here.

Artificial intelligence in translation/localization

With the neural models (mathematical frameworks for machine learning designed to simulate the mechanics of the human mind) coming to the fore, artificial intelligence has become dramatically more sophisticated than ever before. We cannot possibly imagine to what extent and how these advancements will impact our lives in the years to come (You have probably seen all those deepfake videos on the internet, right?), but we may say a few words about the application of AI in translation and localization:

AI needs to be fed with high-quality data

Machine translation constitutes only a tiny fraction of the AI application, but the basic principles apply: if the MT engine is built upon poor quality data (in this case, translation memories of questionable quality featuring mistranslations, spelling errors, segments in wrong languages etc.), there is no way the output results will be satisfactory.

In other words, if you have the professionally developed and reviewed translation assets, you can use these to build a machine translation engine that may generate usable translations and the AI can be “taught” to make predictions that will make sense most of the time.

Imagine that AI engine is the engine of your car. In that case, data is the fuel that powers that engine, so, just like in life, the quality of the output will largely depend upon the quality of the input.

The application of AI in machine translation is used for very specific purposes

AI heavily depends upon the context. This often means that its application in machine translation is used for very specific purposes, i.e. that MT engine must be developed on the basis of your existing translated content and the AI must be fed with the data with all the context issues resolved.

Or, in short, don’t expect generic input and output windows that will provide real-time perfect translation.

AI-powered machine translation is about scale and it is not free of charge

This new technology comes simultaneously with digital content explosion and digital transformation and its true power lies in scale.

It is a perfect solution for the companies and organizations with massive amounts of content for localization and translation (such as governments and corporations) who can afford to invest time and money in data training and development of custom MT engines.

It can help you generate more localized content for less, but it is definitely not free of charge and, while certain organizations can afford to localize internally, the massive amounts of content and language combinations make outsourcing a more reasonable model. Outsourcing partners can provide benefits in terms of scalability, automation, risk management and quality control.

Magic wand has not been invented (yet)

We live in a world of speed, digitalization and technology. It is all great, but one of the downsides of our age is the tendency to oversimplify things and give out false impressions that complex processes can be executed with a double click.

Sadly, this is not true. There is no magic wand that can make any actual work go away.

Technology is here to help us do certain things faster and better, but it is not a substitute for knowledge, creativity and integrity.

And while there is a persistent misconception that machine translation will replace professional human translators any second now, it is only partially true: it will only be able to replace the translators who translate like machines, but that particular type of translator is highly unlikely to find work in the language industry anyway.

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