![]() Language is complex and varied, and the exact same thing can be said and translated in many different ways. If we consider the sentence in question as a translation, it is a very different computing challenge. If we take a simple sentence like, “Today, we are pleased to announce a significant breakthrough with our ongoing MT research, especially as it pertains to Russian to English translations.” In the case of ASR, there is really only one correct answer, the computer either identified the correct word or it did not, and even when it does not properly identify the word, one can often understand from the context and other correctly predicted words.Ĭomputers perform well when problems have binary outcomes, where things are either right or wrong, and computers tend to solve these kinds of problems much more effectively than problems where the “answers” are much less clear. It is perhaps useful to contrast MT to the automated speech recognition (ASR) challenge, to illustrate the difficulty. It is worth some consideration why this is so, as it explains why it has taken 70 years to get here, and why it may still take much more time to get to “always perfect” MT, even in these heady NMT breakthrough days. Recent advances with Neural MT are welcome and indeed significant advances, but MT remains one of the most challenging research areas in AI.Īs the results of 70 years of ongoing MT research efforts show, the machine translation problem is indeed one of the most difficult problems to solve in the Natural Language Processing (NLP) field. This claim to be able to solve the MT problem in five years has been a frequent refrain of the MT community, and almost seventy years later we see that MT remains a challenging problem. The original Georgetown experiment, which involved successful fully automatic translation of more than sixty Russian sentences into English in 1954, was one of the earliest recorded MT projects. Researchers of the Georgetown experiment asserted their belief that machine translation would be a solved problem within three to five years. Its alumni went on to start Google Translate, Moses, influence Amazon MT/AI initiatives, and the company and its intellectual property are now owned by SDL Plc. Warren Weaver inspired the founders of Language Weaver to name themselves after him in the early 2000s, and the company was the first to commercialize and productize Statistical Machine Translation (SMT) and was also the source for much of the subsequent innovation in SMT. What really kick-started research was Cold War fear and the US analysts desire to easily read and translate Russian technical papers. But Weaver’s memo was not the only driver for this emerging field. ![]() ![]() In the famous memorandum referenced here, he said: “it is very tempting to say that a book written in Russian is simply a book written in English which was coded into the Russian code.” These proposals were based on information theory, successes in code-breaking during the Second World War, and theories about the universal principles underlying natural language. The first set of proposals for computer-based machine translation was presented in 1949 by Warren Weaver, a researcher at the Rockefeller Foundation in his now famous " Translation memorandum". One of the earliest mentions of automated translation involves Russian Peter Troyanskii who submitted a proposal that included both the bilingual dictionary and a method for dealing with grammatical roles between languages, based on the grammatical system of Esperanto, even before computers were available. When we consider the history of machine translation, the science by which computers automatically translate from one human language to another, we see that much of the science starts with Russian.
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