The Emergence of Neural Machine Translation: A Game-Changer in Language Translation

Pohan Lin
August 24, 2022
7 min read

What is Neural Machine Translation?

Neural machine translation (NMT) is the latest development in machine translation. It relies on a neural network to decode text, meanwhile learning and developing its language knowledge as it goes. This means it can translate more accurately than translation systems that tackle words and phrases based on preset rules and instructions that have to be manually updated.

Where NMT differs from earlier forms of machine translation is that its neural networks are modeled on how the human brain itself functions, as does a neural network platform.

In particular, our brains tend to make leaps of understanding by predicting meanings and interpretations of words based on repeated patterns and arrangements of words we’ve encountered before. 

NMT’s ability to notice these connections and patterns, coupled with its access to large databases, means that just as with a human brain, over time, some connections get weak while others get strong. This form of machine learning allows the overall language knowledge and depth to grow in sophistication and accuracy over time.

But what exactly is machine translation, and how does it work? Let’s take a look before we dive deeper into Neural Machine Translation. 

What is Machine Translation?

To use and benefit from technology, such as a Resilient Distributed Dataset (RDD), you don’t necessarily have to understand how it works. A grasp of the fundamental principles is helpful. At its most basic, machine translation (MT) means the automatic conversion of text from one language into another. 

It relies on a computer, specifically, AI, to translate. Machine translation originated back in the 1950s, with very basic results which relied on human programmers to impose a set of rules for the technology to follow. But machine translation grew in sophistication in the 21st century and began to take off, particularly for businesses with an online presence.

How Does Neural Machine Translation work?

Of course, ready-made templates have a place for business communication, such as sales proposal examples which are incredibly helpful and time-saving. But with NMT, you are dealing with a system capable of adapting and learning as it turns words in one language into equally impactful words in another.

As we’ve seen, NMT can attain translation accuracy by making predictions based on its previous experience and what it has learned. To do this, it uses a neural network.

Neural networks can handle sizable chunks of data without needing supervision from a human operator. The neural network of an NMT is divided into two: an encoder and a decoder network. Another key component is a series of nodes, like those in a human brain.

The neural network translates text based on predicting meaning and usage; words are decoded from one language and encoded into the target language.

What are the main benefits of NMT for the business?

Of course, the technicalities of how NMT functions are less important than the benefits of this sophisticated type of translation for business users. Businesses care about communicating their messages clearly and having those messages make an impact. 

Customer Service

Of all the areas of business operations, customer service is where good communication is arguably the most vital. It can be a real challenge when talking to customers across cultures, national boundaries, and languages. Not paying enough attention to providing a good translation of conversations is one of the biggest mistakes to avoid entering new markets.

Helpdesks and live chat can both be helped by NMT. Accurate and fast translations of requests, queries, and feedback can all be provided by neural machine translation. The beauty of the system is that NMT gets better and better over time as it learns the particular speech patterns and lexical sets of customers in specific geographic locations.

Again, Machine Translation tools are not good enough to the point where they can replace translators or, in this case, customer service specialists. But they can be helpful in numerous instances. 


While no translation system and MT can guarantee 100% accuracy, NMT has far greater levels than previous incarnations of machine translation. Its ability to predict and learn gives it the edge and ensures more sophisticated and accurate translation.

With MNT, its data bank is constantly growing, enabling it to understand the context of words and to adapt the interpretation accordingly.

However, this ability does rest on the clarity of text and how coherent, technical, or unusual the language to be translated is. Trying to anticipate some technical or unusual language that might arise in certain territories and adding these to the software is good practice before letting the system do its work alone.


Traditional translation with a language specialist is costly, and depending on the volume of content, this cost can escalate hugely. 

NMT has a much lower cost to use daily after its initial purchase and setup. Localization teams often let Machine Translation perform the first pass, and then the translators come in to review and improve what needs to be improved.

Working this way round rather than paying a professional for the entire translation project can help keep translation budgets lower while also saving you time in the process.

Translating the future

NMT is, as we have seen, an evolutionary step in the development of machine translation. There will, without doubt, be further improvements and advances.

But automation of the translation needs of a business doesn’t entirely remove the need for skilled human workers. NMT has its limitations in terms of how companies interact with their consumer base. 

There is still nothing like an actual person with empathy and a sense of humor at the end of a phone line, and some communications are delicate and necessitate extreme accuracy and care with words. In these cases, a human will always do a better job than a machine. 

But that’s not actually the function or main advantage of machine translation. MTs are meant to be used for a quick first pass while translators come in to do what’s left to be done correctly. 

Even if your MT tool gets 1% of translations done correctly, and it’s usually much better than that, this scales up to thousands of translations in a multi-million-word project.

Translation as a specialism will always require a combination of human and machine minds. In a sense, both types of brains contribute different things. A human can’t match a machine for speed and cost-effectiveness. On the other hand, humans can check the accuracy and provide invaluable nuanced language skills crucial to a business’s image and global reach.

Global Expansion, Translation, and NMT

Being a global business means speaking the right language for the right location. Traditionally this has meant an army of human translators, converting copy and content from the original source language into one that can reach a local audience. But Neural Machine Translation may be able to help with that.

Things move fast in our connected world, and this is especially true for business. More and more content is made public on a rolling basis, particularly on social media. Companies need their audiences and customers to understand their messages instantly. 

The answer has been machine translation. Letting technology do the work. And it’s a technology that has evolved and improved since its mid-20th-century origins. The rise of AI has meant even more sophisticated translation technology. There’s no doubt about the benefits of translation software for any business with its eyes on the global market.

Final Thoughts

One final thought on the contribution NMT has the potential to make to any business is to think about its advantage in translating user-generated content, especially content that appears on social media. 

NMT can translate thousands of comments in next to no time, providing a brand with an instant snapshot of how well a campaign or product is being received around the globe. Comments such as these can tell a brand what the consumer base values or dislikes about its products or image. 

This information is valuable. It’s a gauge of what customers think and feel. In this way, NMT can reveal what has been lost in translation, or if a brand is truly speaking its customers’ language.

This article is a contribution by Pohan Lin from DataBricks, who has also been published in domains such as SME-News.

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Pohan Lin
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks. Databricks is a global AI provider connecting the features of databricks hadoop configuration, data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing.
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