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This is then followed by the review of a human translator who often has to rewrite parts of the machine-translated content (“post-editing”). And even the translation industry itself is using machine translation: either for text where translations can look less polished or for merely a first round of translation. People have increasingly gotten used to machine translation – their expectations might be slightly lower these days than in 1954 and the results often meet people’s needs. According to Google, the Google Translate site ( ) process enough translations on one day to fill one million books. Using machine translation for text has become fairly commonplace. Still, the new statistical or data-driven machine translation was and is often still augmented by a few specified rules such a mix of data-driven and rules-based MT is called hybrid MT. This new approach made it a lot easier now to add new language pairs: since no or little hand-coding was needed, no language experts needed to get hired. Statistical word association methods could be used, meaning that instead of trying to have computers figure out grammatical structures, it was now only deemed necessary to understand the probability with which a word was used in a certain form surrounded by certain other words. Huge digitally available text corpora in multiple languages – like the many translations for laws of the European Union – were used the same way. With the worldwide web a lot of bilingual or even multilingual content became available (for example several translated versions of a multinational company’s homepage), and these translations could be mapped to each other to be leveraged for machine translation. The advent of the Internet dramatically changed the designs of machine translation. For a translation, the source sentence was analyzed using a linguistic parser then the source grammar was determined, subsequently mapped to the target grammar and finally the target language’s grammatical structure was created, while the meaning of the individual words was retrieved from a big lexicon.
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This convinced the US government to invest more in research of machine translation. However, fast results were not to be had: the claim of the authors that within three to five years, machine translation would work seamlessly did not come to be true.Īll the early MT relied on linguistic knowledge: language experts encoded grammar rules in a way that they could be transformed into another language (Therefore this machine translation is called rules-based MT). In 1954, Georgetown University and IBM performed a joint demonstration of machine translation, having a computer translate more than sixty Russian sentences into English. In the beginning of the 1950s, scientists pushed forward with MT in the United States to help with understanding what Soviet scientists and generals were writing in Russian (and in the Soviet Union, researchers tried the same for English, with the same motivation: knowing what the enemy in the Cold War was up to). The first efforts to have computers translate from one language to another started in the early days of computing. Machine Translation (MT) has been around for some time. Skype is using that of Microsoft, which also powers Bing Translator ( ). Skype Translator allows Skype users to have their instant messages automatically translated from one language to another (for 50 languages) and for seven languages, users can even have the spoken content of their phone or video calls translated.įor the translation of instant messages (as for text in general) all that is needed is a good machine translation engine. One of the products that aim at helping people understand each other in different languages is Skype Translator. It is therefore no surprise that people have been looking to tear down the language barrier – with the help of computers that can now solve complex linguistic challenges. But having different languages – up to 7,000, linguists estimate – is also a major obstacle, in this age of globalization in which travelling and trade have become so much easier. Beautiful is the diversity of languages, and the richness of human traditions they reflect.