The Impact of Google Neural Machine Translation on Market Globalization

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We’ve all heard of Google Translate. For many people and companies it has become the go-to tool when they need their words translated to other languages. Even translators will often use it as it to get some of the basic translations out of the way and they can focus on the difficult stuff. It can translate a surprisingly large number of languages already. Using 150 different programs, it can translate about 103 languages and dialects.

At the same time, Google Translate does leave something to be desired. Though it might do better than most automated translation programs, it is still left in the dust by human translators. That’s because it doesn’t understand nuance. Often, it will treat words that are semantically similar as identical. And that can lead to a loss of meaning and implication.

Welcome to the next step: Google Neural Translation Machine

That’s where Google’s Neural Translation Machine (or GNTM for short) comes in. It’s the next step in translation. It’s a proper neural network, which means that it mimics the way the human brain has been set up, with nodes interconnected and strengthening or weakening based on learning. This means it can take in more information from different sources and integrate them into a cohesive whole, which can understand far more and get at that nuance I mentioned before.

An example of what GNTM has been capable of is that it has managed to eliminate the middle step. With most traditional translation programs online, what happens is that if you’re translating from any one language to another, you go through English. So, for example, if you’re translating from German to Greek, there will be an in between step where the translation passes through English.

The GNTM does not do that. That step through English has been eliminated. Even more impressively, it has managed to do so all on its own. That’s a big deal. After all, every time you move through another language, some original meaning will get lost as words and sentence structures do not map onto each other perfectly. By eliminating one such step you therefore remove a lot of inaccuracy.

More tellingly, because the program did it all on its own, you can easily see that there is a huge amount of scope for it to improve how it translates. It really understands (as far a computer can) the language that is being used. And with that kind of understanding, we’re bound to see a lot more innovation.

What that will mean for market globalization

There will be two big changes. The one which will happen on the global stage, where barriers to entry raised by incompatible languages will come down. The second is within the translations industry itself. Now, to be clear. Translation companies will hardly go the way of the dodo in the next ten years. At the same time time, there will be a shock to the system. Smaller outfits with a weak reputation will probably suffer as these neural networks eat into their market share. As a result, we might well see a shrinkage of the translation agencies list.

For bigger firms, however, these technologies might offer a real boon. For example, they will make translation much faster. Instead of translators needing to translate every word, they will become more like magazine editors who check if the general flow is correct and here and there choose different structures which mean slightly different things. What’s more, they might even see the work grow, as smaller companies find it easier to enter into foreign markets due to the lowered language barriers.

As for other types of companies, these types of technologies will offer a huge boon. Simple translations, for example, will become both cheap and effective. This will allow smaller companies who traditionally don’t have a budget for translation, to suddenly start eying markets beyond their native language.

One of the groups that looks to benefit most are companies in developing countries with an interest in taking steps onto the international stage. After all, often they are working on a more limited budget and can’t afford wide-ranging human translation. This meant that traditionally their scope for growth was limited, as they often have much smaller native-language audience to pull in as customers. Cheap and effective translations technologies will make it far easier for them to make forays into foreign markets – be it in neighboring countries or into the international English-speaking communities.

Of course, with the costs of translation coming down, international companies will also find new markets opening up to them. For example, it will become interesting to move into language markets that were formerly too small to be of real interest.

Another area where we might see some dramatic change are in such areas as service industries. Traditionally, many service industries are more language-dependent than other companies. This often restricts them to one language region. But, with the advance of these types of technologies that’s sure to change. For example, we might see more news outfits translate their articles into additional languages. The same could be true of many online outfits – particularly smaller groups which previously found the costs of translation prohibited them from considering such thoughts.

Beyond language translation

Of course, the effects of these neural networks will not restrict themselves to language translation. There are a huge number of similar problems where the lessons learned from translation will have a massive impact. For example, taking visual signals and turning those into a picture that computers can understand is similarly an exercise in translation. The same goes for audio signals and understanding the spoken language as well.

And so, what outfits like Google are learning through language translation is sure to be applied to other areas as well. With the advancement of neural networks in language translation, we’ll soon be dealing with advancements in other areas as well. Whether that will be in self-driving cars, language-recognition, or code-writing software is what we’ll have to find out.

But we can say for certain, as these neural networks advance, the world it is a changing.


About the Author:

Dina Indelicato is a blogger enthusiast and freelance writer. She is always open to research about new topics and gain new experiences to share with her readers. You can find her on Twitter @DinaIndelicato and LinkedIn.


We are influencers and brand affiliates.  This post contains affiliate links, most which go to Amazon and are Geo-Affiliate links to nearest Amazon store.