Neural Machine Translation Versus Statistical Machine Translation: Which is Better?

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Research findings in artificial intelligence have considerably improved machine translation accuracy and performance. Since translation is a significant component in the marketing hierarchy of any business, related technological advances are welcome news. 

The introduction of AI translator services and neural machine translations sparked hope that one day, computers may flawlessly translate between different languages. 

Statistical Machine Translations

Before the year 2016, computer translations were done in a phrase-based manner. Any linguist is aware that this method is more accurate compared to a word-for-word translation. 

Then again, this method still had many translation errors. The translation algorithms of SMT was improved with a specific language’s sentence order and popular terms. 

This, unfortunately, led to outputs that changed the whole meaning of the sentence. For this reason, hiring professional translators to tackle business-related translation tasks seemed more prudent. 

Neural Machine Translation 

In November 2016, numerous large companies hosted machine translations that used neural-based systems. Back then, consumers did not know how to improve the quality and accuracy of their translation outputs. Computer science experts and linguists worked hard for many years to build a translation system that also learns and grows with time. 

The first and basic concept of this technology was contemplated and conceived in the early 1990s. During that time, Speech Recognition was introduced. SR is equipped with the ability to learn the intonation and voice of a user. This ability was later applied to various software programs. 

Just like SR, NMT is a fluid software that learns the inputs that it continuously receives as time passes. It’s also equipped with the capacity to learn from its developer directly. Both of these capacities spurred the expansion of translation services. Now, translation is no longer limited to vocabulary. It’s also capable of translating syntax and grammar.  

NMT versus SMT: How are they different? 

Microsoft and Google, two of the biggest tech companies in the world, use algorithms based on NMT for their own translation systems. This fact alone suggests that NMT is inherently more beneficial compared to SMT. 

So, what makes NMT a superior AI translator system compared to SMT?

NMT can handle morphology, word order, agreements, and syntax five times better than SMT systems. NMT translations are also more precise and fluent compared to translations of SMT systems. 

NMT needs more of the server’s physical resources, but this can be justified by the knowledge volume that it takes into account for each translation task. An NMT system usually takes weeks to “train” compared to SMT. This is because NMT incorporates and accesses broader data point ranges for every translation sequence. 

With all these comparisons, it’s clear that neural machine translation is better and more reliable compared to statistical machine translation. The good news is most language translation companies today use AI translation software that either incorporates or leverages NMT and SMT systems. 

The continuous advancements in language translations only mean that neural-like learning and AI can only expand the learning capacities of computers. AI can be a powerful tool in developing, managing and learning for individuals and businesses. 

We live in an exciting era. The current breakthroughs only suggest that technology will only get more sophisticated, impressive, and useful from here.

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