The Early Years
The first proposals for machine translation using computers were put forward by Warren Weaver, a researcher at the Rockefeller Foundation, in his July, 1949 memorandum. These proposals were based on information theory, successes of code breaking during the second world war and speculation about universal underlying principles of natural language.
A few years after these proposals, research began in earnest at many universities in the United States. On 7 January 1954, the Georgetown-IBM experiment, the first public demonstration of an MT system, was held in New York at the head office of IBM. The demonstration was widely reported in the newspapers and received much public interest. The system itself, however, was no more than what today would be called a "toy" system, having just 250 words and translating just 49 carefully selected Russian sentences into English — mainly in the field of chemistry. Nevertheless it encouraged the view that machine translation was imminent — and in particular stimulated the financing of the research, not just in the US but worldwide.
Early systems used large bilingual dictionaries and hand-coded rules for fixing the word order in the final output. This was eventually found to be too restrictive, and developments in linguistics at the time, for example generative linguistics and transformational grammar were proposed to improve the quality of translations.
During this time, operational systems were installed. The United States Air Force used a system produced by IBM and Washington University, while the Atomic Energy Commission in the United States and Euratom in Italy used a system developed at Georgetown University. While the quality of the output was poor, it nevertheless met many of the customers' needs, chiefly in terms of speed.
At the end of the 1950s, an argument was put forward by Yehoshua Bar-Hillel, a researcher asked by the US government to look into machine translation against the possibility of "Fully Automatic High Quality Translation" by machines. The argument is one of semantic ambiguity or double-meaning. Consider the following sentence:
- Little John was looking for his toy box. Finally he found it. The box was in the pen.
The word pen may have two meanings, the first meaning something you use to write with, the second meaning a container of some kind. To a human, the meaning is obvious, but he claimed that without a "universal encyclopedia" a machine would never be able to deal with this problem. Today, this type of semantic ambiguity can be solved by writing source texts for machine translation in a controlled language that uses a vocabulary in which each word has exactly one meaning.
Read more about this topic: History Of Machine Translation
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