Most global businesses use machine translation systems for their localization projects. It does not just save a lot of time and effort but is also a cost-effective way to generate translation with the fastest turnaround time. Although, there is still a debate about the credibility of machine translations and whether they are really capable of producing good translations like human translators.
Well, the quality of translation that a machine produces is highly dependent on how you build a machine translation system. You can set up your own machine translation software to generate more in-context translations for foreign audiences.
If you have confusion on whether you need a machine translation system or not, then read this article till the end. Here we’ll discuss the MT, its working, and the benefits that you can get by using it.
Machine Translation (MT) is a process of automatically translating the content using a translation system that uses artificial intelligence technology. It allows you to translate your content to any language without any guidelines from the human.
The concept of machine translation started in the 1950s when computing experts made one of the first translation applications. However, the computer scientists didn’t estimate that the translation tasks could be so complicated, which required a powerful data processing system with a high capacity of storage. Early computers were not that powerful to perform translation effectively.
But time has changed now, with the advancement of technology, we have made such systems that can perform accurate translations seamlessly. Till then, Statistical Machine Translation (SMT) has been used for generating translations.
Later on, the Neural Machine Translation (NMT) model was introduced to create high-quality, in-context translations. In an experiment conducted in 2016, when Google’s team tested the NMT model against its statistical machine translation engine. This experiment showed positive results and NMT generated translations faster, and there was a visible improvement in quality as well.
The neural machine learning experiment turns out very well that Google has implemented it as their development model. After Google, many other big companies followed, and this machine translation has become a valuable addition to modern translation technology. The majority of professional cloud based translation management system now integrate MT into their solution to improve the quality of translation.
Machine translation system produces translations by comparing and matching the large volumes of source and target languages through a machine translation engine. There are three different machine translation methods used by TMSs, which are as follow:
It is a traditional method used for translations. This method of the translation doesn’t rely on linguistic rules. It has a large database of existing human translations, and analyzing these translations, generates new ones. Statistical Machine Translation (SMT) works with training algorithms and produces the translation by evaluating the most used words in that language.
The purpose of this system is to produce the best translation possible based on its existing translation collection. To work with SMT you would need expansive hardware configuration. This model might not work well with all languages. However, it is a good choice for translating languages that have the same word order.
By using a huge neural network, this method works on a deep learning model that continuously learns to improve its translation. This method can generate more quality translation quickly. Neural Machine Translation (NMT) is getting popular among global businesses because of its capability to translate in context, considering all linguistic and cultural aspects. The NMT model works very much like your brain, and it gets wiser gradually as you use it. However, this model is quite expensive comparatively.
It is an advanced method for translation, designed by language experts and linguists. It utilizes bilingual dictionaries and follows all grammatical and linguistic rules. The best thing about this method is that you can customize it based on the specific needs of each industry.
So, your MT system would use any of the above-mentioned methods for the translation based on its specifications. To sustain your translation quality, you have to make a continuous investment with an initial fee. If you don’t want to lose the context of the original text then Rules-Based Machine Translation is something you can consider.
Unlike human translators, MT can manage huge volumes of content, and its automated workflow ensures faster translation processes. You will save a sufficient amount of time, as you can translate the complete document of translation within seconds. And, of course, the content translated by MT would need editing and proofreading by human translators before approving them.
MT costs you way much lower than human translators. Most MT systems even allow you to translate content without any human involvement. You can translate a massive amount of content on MT systems at very less cost, as compared to human translations.
Advanced MT systems also have built-in translation memories that allow them to store the previously approved translation document and recycle them to generate new ones. It not just saves time but also helps you generate in-context translations that are grammatically accurate and culturally appropriate.
The demand for Machine Translation has increased over the past decade because of rapid globalization. You can build a machine translation system tailored to your unique business requirements that would fulfill your specific industry needs. MT is very useful to enhance the productivity and performance of your translation team. Especially for the language service providers who have to translate large amounts of content into multiple languages, the MT system can be so useful. Make sure you are buying your MT system from a reliable service provider, as it has a long-term impact on your global business translation management projects.
