![]() ![]() By analyzing billions of examples, programs are able to recognize patterns in the translation from one language to another. Statistical MT – This form of machine translation relies entirely on data created by humans.In part, this is through the emergence of two separate types of MT technology: The past decade can be viewed as a great leap forward for machine translation. And more recently, Neural Machine Translation has improved the quality of MT immeasurably. By 2012, Google Translate was processing enough text to fill one million books every day. Exactly ten years later, an open-source statistical MT engine named MOSES was launched. By 1997, AltaVista Babel Fish was racking up half a million requests every day. With millions of people uploading content and searching online every day, researchers had vast amounts of data for training algorithms.Ī couple of automated systems launched online in the late 1990s. It was the introduction of the web that really changed machine learning for the better. It was only in 1991 that the first commercial machine translation system was released by Kharkov State University, Ukraine. In the following 20 years, research continued but progress stagnated. In 1964, teams from around the world assembled for the first MT international convention. gave a public demonstration of a working system. By 1954, a team of researchers from Georgetown University in Washington D.C. He didn’t have to wait long for an answer. He wondered whether it would be possible to use technology, not only to decrypt Russian documents, but also to translate them. Weaver had worked on cryptography in WWII. The first proposal for machine translation was made in 1947 from the American mathematician, Warren Weaver, to his cyberneticist friend, Norbert Wiener. However, the process of developing this technology has also been an epic journey. The idea of using a computer to translate from one language to another has been around for longer than you might think. Here’s a look at the state of machine translation - and why your business probably still needs authentic human translation. In some senses, this has been happening for some time.īut just as the voice in your phone can’t yet replace a personal assistant, machine translation (MT) has a way to go. It would be natural to assume that similar technology could be applied to translation. Voice assistants with these capabilities were the stuff of science fiction just a few years ago. Siri and Alexa are gleaming examples of the progress that has been made. Software has become much, much better at interpreting language over the past decade. ![]()
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