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In 1965, the AMTCL took over the journal Mechanical Translation and Computational Linguistics. This journal was succeeded by many other journals: the American Journal of Computational Linguistics (1974–1978, 1980–1983), and then Computational Linguistics (1984–present). [8] Since 1988, the journal has been published for the ACL by MIT Press.
The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation. [3]
Machine translation enjoyed a fierce research interest during the 1950s and 1960s, which was followed by a stagnation until the 1980s. [7] After the 1980s, machine translation became mainstream again, enjoying an even bigger popularity than in the 1950s and 1960s as well as rapid expansion, largely based on the text corpora approach.
One of the constituent parts of the ALPAC report was a study comparing different levels of human translation with machine translation output, using human subjects as judges. The human judges were specially trained for the purpose. The evaluation study compared an MT system translating from Russian into English with human translators, on two ...
The Forward Area Language Converter (FALCon) system, a machine translation technology designed by the Army Research Laboratory, was fielded 1997 to translate documents for soldiers in Bosnia. [16] There was significant growth in the use of machine translation as a result of the advent of low-cost and more powerful computers.
The institute is home to 33 faculty with the primary scholarly research of the institute focused on machine translation, speech recognition, speech synthesis, information retrieval, parsing, information extraction, and multimodal machine learning. Until 1996, the institute existed as the Center for Machine Translation, which was established in ...
Said to be probably the single most influential publication in the early days of machine translation, it formulated goals and methods before most people had any idea of what computers might be capable of, and was the direct stimulus for the beginnings of research first in the United States and then later, indirectly, throughout the world.
Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.