If you’re involved with translation or localization, you already know what machine translation is. You know that it offers a tremendous set of benefits, mainly in terms of saving money and time. However, one problem that a lot of marketers, translators, and product managers face is evaluating different types of machine translation and choosing one that suits their needs the best.
There are four types of machine translation– Statistical Machine Translation (SMT), Rule-based Machine Translation (RBMT), Hybrid Machine Translation, and Neural Machine Translation. But, before we explore these four, let’s a get a few things straight.
You see, machine translation is primarily a tool that helps marketers/translators achieve a goal. It is not a replacement for the older systems of translation. Rather, it’s an enhancement. For instance, in a traditional localization cycle, we encounter what is called the TEP phase. TEP here stands for ‘translate, edit, and proof.’
Now, in a TEP cycle, the role of machine translation starts and ends with ‘T,’ which is ‘translation.’ The rest of the work, which is editing and proofing, still needs to be carried out by professional translators and language experts.
But, the goal is still the same, irrespective of which approach you follow. The client must be provided with top-notch translation work. Attention to style, terminology, and syntax are significant in the localized results. This is why machine translation engines require them as input.
But, machine translation is still highly capable when it comes to generating savings, which is a benefit that most wouldn’t want to miss out on.
Plus, the sheer volume of content that needs to be processed and managed out there necessitates the need for unique technological solutions. To make things even more complicated, the turnover time today is drastically low. Human effort simply will not cut it. Integrating machine translation into the localization strategy is a must now. There is no room for choice.
Speaking of choice, which type of machine translation should you opt for? Well, let’s find out.
Statistical Machine Translation (SMT)
SMT works by referring to statistical models that are based on the analysis of large volumes of bilingual text. It aims to determine the correspondence between a word from the source language and a word from the target language.
A good example of this is Google Translate. Now, SMT is great for basic translation, but its greatest drawback is that it does not factor in context, which means translations can often be erroneous. In other words, don’t expect high-quality translations.
Rule-Based Machine Translation (RBMT)
RBMT, on the other hand, translates on the basis of grammatical rules. It conducts a grammatical analysis of the source language and the target language to generate the translated sentence. However, RBMT requires extensive proofreading, and its heavy dependence on lexicons means that efficiency is achieved after a long period of time.
Hybrid Machine Translation (HMT)
HMT, as the term indicates, is a blend of RBMT and SMT. It leverages a translation memory, making it far more effective in terms of quality. However, even HMT has its share of drawbacks, the greatest of which is the need for extensive editing. Human translators will be required.
Neural Machine Translation (NMT)
NMT is a type of machine translation that depends on neural network models (based on the human brain) to develop statistical models for the purpose of translation. The primary benefit of NMT is that it provides a single system that can be trained to decipher the source and target text.
As a result, it does not depend on specialized systems that are common to other machine translation systems, especially SMT.
Which do you choose?
To put it simply, NMT is an end-to-end translation system. As you can see, it can be quite difficult to determine the ideal machine translation approach for your business. Each offers its own set of pros and cons. When evaluating types of machine translation, it ultimately boils down to your own needs.
But, SMT is the most preferred approach today. This is because of the rules of language change which impacts the RBMT approach. These rules need to be constantly updated. SMT, however, does not depend on rules and its systems can be constructed in much less time compared to RBMT systems.
The training data needed to run SMT is also widely available on the Internet due to the publication of multilingual content. Finally, its need for a high amount of processing power is also easy to meet now, thanks to cloud solutions.
NMT, on the other hand, is definitely the most advanced option here. However, training models for NMT is an expensive affair, which means small-to-medium-sized businesses will have to consider the cost to profit ratio.