Machine translation

translation softwareautomatic translationtranslationautomated translationmachine-translatedmachine translation systemmechanical translationtranslation platformautomate translationautomated language translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.wikipedia
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Computer-assisted translation

computer-aided translationcomputer assisted translationCAT
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
Computer-assisted translation is sometimes called computer-aided, machine-assisted, or machine-aided, translation (not to be confused with machine translation).

Computational linguistics

mathematical linguisticscomputational linguistSymbolic Systems
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
When machine translation (also known as mechanical translation) failed to yield accurate translations right away, automated processing of human languages was recognized as far more complex than had originally been assumed.

Warren Weaver

WeaverWeaver, W.Weaver, Warren
The field of machine translation was founded with Warren Weaver's Memorandum on Translation (1949).
He is widely recognized as one of the pioneers of machine translation, and as an important figure in creating support for science in the United States.

SYSTRAN

Peter Toma
The French Textile Institute also used MT to translate abstracts from and into French, English, German and Spanish (1970); Brigham Young University started a project to translate Mormon texts by automated translation (1971); and Xerox used SYSTRAN to translate technical manuals (1978).
SYSTRAN, founded by Dr. Peter Toma in 1968, is one of the oldest machine translation companies.

Yehoshua Bar-Hillel

Bar-HillelBar-Hillel, YehoshuaBar Hillel
Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality, first and most notably by Yehoshua Bar-Hillel.
He is perhaps best known for his pioneering work in machine translation and formal linguistics.

Statistical machine translation

statisticalstatistical models for machine translationcorpus-based MT
Beginning in the late 1980s, as computational power increased and became less expensive, more interest was shown in statistical models for machine translation.
Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora.

Georgetown–IBM experiment

Georgetown-IBM experimentGeorgetown experimentGeorgetown
A Georgetown University MT research team followed (1951) with a public demonstration of its Georgetown-IBM experiment system in 1954.
The Georgetown–IBM experiment was an influential demonstration of machine translation, which was performed during January 7, 1954.

ALPAC

ALPAC reportAutomatic Language Processing Advisory Committee
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.
ALPAC (Automatic Language Processing Advisory Committee) was a committee of seven scientists led by John R. Pierce, established in 1964 by the United States government in order to evaluate the progress in computational linguistics in general and machine translation in particular.

Translation

translatortranslatedtranslators
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
Because of the laboriousness of the translation process, since the 1940s efforts have been made, with varying degrees of success, to automate translation or to mechanically aid the human translator.

Interlingual machine translation

Interlingua
According to the nature of the intermediary representation, an approach is described as interlingual machine translation or transfer-based machine translation.
Interlingual machine translation is one of the classic approaches to machine translation.

Interactive machine translation

interactive translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
In this project, the human interaction was aimed towards producing the target text for the first time by embedding data-driven machine translation techniques within the interactive translation environment with the goal of achieving the best of both actors: the efficiency of the automatic system and the reliability of human translators.

Natural-language understanding

natural language understandinglanguage understandingUnderstanding
It is often argued that the success of machine translation requires the problem of natural language understanding to be solved first.
There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

Transfer-based machine translation

deep transfertransfertransfer approach
According to the nature of the intermediary representation, an approach is described as interlingual machine translation or transfer-based machine translation.
Transfer-based machine translation is a type of machine translation (MT).

Word-sense disambiguation

word sense disambiguationdisambiguationdisambiguate
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 proper names.
For example, the ambiguity of 'mouse' (animal or device) is not relevant in English-French machine translation, but is relevant in information retrieval.

Rule-based machine translation

rule-basedRBMTrule-based approaches to machine translation
To translate between closely related languages, the technique referred to as rule-based machine translation may be used.
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.

Text corpus

corpuscorrespondencecorpora
For example, the large multilingual corpus of data needed for statistical methods to work is not necessary for the grammar-based methods.
Machine translation algorithms for translating between two languages are often trained using parallel fragments comprising a first language corpus and a second language corpus which is an element-for-element translation of the first language corpus.

Deep learning

deep neural networkdeep neural networksdeep-learning
A deep learning based approach to MT, neural machine translation has made rapid progress in recent years, and Google has announced its translation services are now using this technology in preference to its previous statistical methods.
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Makoto Nagao

Example-based machine translation (EBMT) approach was proposed by Makoto Nagao in 1984.
He contributed to various fields: machine translation, natural language processing, pattern recognition, image processing and library science.

Neural machine translation

neural machineneural translationsequence-to-sequence learning
A deep learning based approach to MT, neural machine translation has made rapid progress in recent years, and Google has announced its translation services are now using this technology in preference to its previous statistical methods.
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

BLEU

BLEU metric
If named entities cannot be recognized by the machine translator, they may be erroneously translated as common nouns, which would most likely not affect the BLEU rating of the translation but would change the text's human readability.
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.

Corpus linguistics

corpuscorporacorpus analysis
Solving this problem with corpus statistical, and neural 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.

Natural language processing

NLPnatural languagenatural-language processing
In NLP, ontologies can be used as a source of knowledge for machine translation systems.
The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English.

Hansard

HL DebHC Debparliamentary reports
Statistical machine translation tries to generate translations using statistical methods based on bilingual text corpora, such as the Canadian Hansard corpus, the English-French record of the Canadian parliament and EUROPARL, the record of the European Parliament.
This makes it a natural parallel text, and it is often used to train French–English machine translation programs.

Ontology (information science)

ontologyontologiesOntology (computer science)
An ontology is a formal representation of knowledge which includes the concepts (such as objects, processes etc.) in a domain and some relations between them.
Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI.

Artificial intelligence

AIA.I.artificially intelligent
The ideal deep approach would require the translation software to do all the research necessary for this kind of disambiguation on its own; but this would require a higher degree of AI than has yet been attained.
Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation.