In my next series of articles, I will discuss Statistical MT and Neural MT with example implementations. I have attached reference links below for further reading on these concepts. Several approaches have evolved over time, and researchers are constantly working to develop more useful machine translation systems. Professor Vauquois developed this concept in 1968, demonstrating visually how machine translation could be done at the time. Imagine trying to read or respond to what’s app message/twitter message in another language, and everytime you require a language translator besides you.
Active Research in this area started since as far back as the 1950s c.a. The concept of machine translation arose from the need for faster translation in native dialects without the use of a human translator. The texts and messages that were passed around by enemies or collaborators piqued the interest and curiosity of warring factions. Language translators are essential in bridging communication gaps for meetings, legal documents, and new laws, among other things.Īnother interesting fact about translation is that it became more popular after World War II. The European Parliament, which speaks over 20 languages and has over 700 members, is an excellent example of this. Is this human resource readily available at all times and in all circumstances? Typically, Without machine translation, a human translator who speaks both languages is required to facilitate communication between two dialect speakers.
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