Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation MAHT and interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.
On a basic level, MT performs simple substitution of words in one natural language for words in another, but that alone usually cannot produce a good translation of a text, because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus and statistical techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.
Current
machine translation software often allows for customization by domain or profession
(such as weather reports), improving output by limiting the scope of allowable
substitutions. This technique is particularly effective in domains where formal
or formulaic language is used. It follows that machine translation of
government and legal documents more readily produces usable output than
conversation or less standardised text.
Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).
The progress and potential of machine translation has been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality. Some critics claim that there are in-principle obstacles to automatizing the translation process.
The idea of machine translation may be traced back to the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. In the 1950s, The Georgetown experiment (1954) involved fully automatic translation of over sixty Russian sentences into English. The experiment was a great success and ushered in an era of substantial funding for machine-translation research. The authors claimed that within three to five years, machine translation would be a solved problem.
Real progress was much slower, however, and after the
ALPAC report (1966), which found that the ten-year-long research had failed to
fulfill expectations, funding was greatly reduced. Beginning in the late 1980s,
as computational power increased and became less expensive, more interest was
shown in statistical models for machine translation.
The idea of using digital computers for translation of natural languages was proposed as early as 1946 by A. D. Booth and possibly others. Warren Weaver wrote an important memorandum "Translation" in 1949. The Georgetown experiment was by no means the first such application, and a demonstration was made in 1954 on the APEXC machine at Birkbeck College (University of London) of a rudimentary translation of English into French. Several papers on the topic were published at the time, and even articles in popular journals (see for example Wireless World, Sept. 1955, Cleave and Zacharov). A similar application, also pioneered at Birkbeck College at the time, was reading and composing Braille texts by computer.
The idea of using digital computers for translation of natural languages was proposed as early as 1946 by A. D. Booth and possibly others. Warren Weaver wrote an important memorandum "Translation" in 1949. The Georgetown experiment was by no means the first such application, and a demonstration was made in 1954 on the APEXC machine at Birkbeck College (University of London) of a rudimentary translation of English into French. Several papers on the topic were published at the time, and even articles in popular journals (see for example Wireless World, Sept. 1955, Cleave and Zacharov). A similar application, also pioneered at Birkbeck College at the time, was reading and composing Braille texts by computer.
“Banyak orang tidur hanya 4-5 jam sehari tapi tetap sehat, lantaran
tidurnya berkualitas. Jadi memang dalam kasus begadang, kita tak bisa
hanya melihat satu sisi begadangnya saja. Tapi harus dilihat secara
keseluruhan, bagaimana olahraganya, waktu istirahat, dan pemenuhan
nutrisinya,” kata Sarah.
Yang terpenting, lanjut Sarah, bagi orang yang terpaksa begadang harus
memerhatikan kebutuhan nutrisi dan olahraga minimal setengah jam sehari
sesuai standardisasi Organisasi Kesehatan Dunia (WHO). “Selain
memperhatikan nutrisi, berolahraga, hindari merokok dan minum alkohol.
Jika itu dilakukan maka dia akan tetap sehat,” tandasnya.