From its origins, machine translation meant a radical change in the accessibility of people to a globalized community. The truth is that today we just need a couple of clicks to communicate with someone in a language that perhaps we didn’t even know existed. Continue reading Identify Any Language With This API In Only Seconds, we will tell you about Text Translation and Language Detector API, an excellent translation tool that will allow you to obtain high-quality results in a few seconds.
The automated process of translating original content into another language without the involvement of human translators is known as machine translation. Machine translation has been around for decades, but to the general public, it is still a relatively new idea.
What factors influenced the evolution of machine translation?
Statistical models and hybrid engines
The biggest development in machine translation occurred during the 1990s when companies like IBM began to take advantage of statistical models that significantly improved translation quality. Statistical machine translation engines were new technology. These engines focused on the use of advanced statistical methods and large volumes of data from the Internet to translate an increasing amount of content. Later, Google would implement the technology on a large scale to make all human knowledge searchable.
Early statistical machine translation engines were much better than rule-based engines, but they still made a lot of mistakes. Therefore, companies began experimenting with hybrid machine translation engines, which typically combined statistical machine translation with rule-based machine translation. These advances popularized machine translation technology and helped it to be adopted on a global scale.
Neural machine translation
In 2017, machine translation took another technological leap with the arrival of Neural Machine Translation (NMT). Neural machine translation harnesses the power of artificial intelligence (AI) and uses neural networks to generate translations.
Compared to the aforementioned methods, neural networks try to mimic a translator’s thought process instead of “guessing” a likely outcome. The result is a much more natural-sounding translation, capturing the meaning and nuances of the sentence more accurately. This development made machine translation good enough, not only for understanding or capturing the gist of large volumes of documents but also for the translation of regular, non-mission-critical business documents.
Neural machine translation addressed some of machine translation’s longstanding shortcomings, including poor readability of machine translations and its incompatibility with some languages, such as Korean. Efforts to improve neural machine translation are constant. To learn more about Neural Machine Translation, read our blog post-Neural Machine Translation: How Artificial Intelligence Works in Language Translation.
Identify Any Language With Text Translation and Language Detector API
This API’s goal is to assist you in determining the language of any text you provide it with. Additionally, you will have the option of dynamically translating your preferred texts. Text Translation and Language Detector API is perfect for businesses or users who deal with international traffic. Displaying your content in the language of your choice will help you provide different options for different users. Additionally, translating those texts will assist you in expanding the audience for your content.
What this API receives and what your API provides (input/output)?
Just pass the text that you want to translate or detect the language from. You will be receiving either the language or the new text translated.
If You Want To Learn More About This, Read…
What Is A Text Translation And Language Detector API?
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