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distributions of "Ted" and "Erica" individually, so that the probability of a given name in a specific language will not affect the assigned probability of a translation. A study by
Stanford on improving this area of translation gives the examples that different probabilities will be assigned to "David is going for a walk" and "Ankit is going for a walk" for English as a target language due to the different number of occurrences for each name in the training data. A frustrating outcome of the same study by Stanford (and other attempts to improve named recognition translation) is that many times, a decrease in the
1450:(ASL) translations. The program would first analyze the syntactic, grammatical, and morphological aspects of the English text. Following this step, the program accessed a sign synthesizer, which acted as a dictionary for ASL. This synthesizer housed the process one must follow to complete ASL signs, as well as the meanings of these signs. Once the entire text is analyzed and the signs necessary to complete the translation are located in the synthesizer, a computer generated human appeared and would use ASL to sign the English text to the user.
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resolve. For instance, the author of the source text, an
Australian physician, cited the example of an epidemic which was declared during World War II in a "Japanese prisoners of war camp". Was he talking about an American camp with Japanese prisoners or a Japanese camp with American prisoners? The English has two senses. It's necessary therefore to do research, maybe to the extent of a phone call to Australia.
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48:
1283:. Lawyers who use free translation tools such as Google Translate may accidentally violate client confidentiality by exposing private information to the providers of the translation tools. In addition, there have been arguments that consent for a police search that is obtained with machine translation is invalid, with different courts issuing different verdicts over whether or not these arguments are valid.
697:(the future head of Translation Development AT Google) won DARPA's speed MT competition (2003). More innovations during this time included MOSES, the open-source statistical MT engine (2007), a text/SMS translation service for mobiles in Japan (2008), and a mobile phone with built-in speech-to-speech translation functionality for English, Japanese and Chinese (2009). In 2012, Google announced that
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sentences are analyzed accuracy is jeopardized. Researchers found that when a program is trained on 203,529 sentence pairings, accuracy actually decreases. The optimal level of training data seems to be just over 100,000 sentences, possibly because as training data increases, the number of possible sentences increases, making it harder to find an exact translation match.
648:(1966), which found that the ten-year-long research had failed to fulfill expectations, funding was greatly reduced. According to a 1972 report by the Director of Defense Research and Engineering (DDR&E), the feasibility of large-scale MT was reestablished by the success of the Logos MT system in translating military manuals into Vietnamese during that conflict.
1428:) being repeatedly pasted into Google Translate, with the resulting translations quickly degrading into nonsensical phrases such as "DECEARING EGG" and "Deep-sea squeeze trees", which are then read in increasingly absurd voices; the full-length version of the video currently has 6.9 million views as of March 2022.
862:, that is a body of text that has been translated into 3 or more languages. Using these methods, a text that has been translated into 2 or more languages may be utilized in combination to provide a more accurate translation into a third language compared with if just one of those source languages were used alone.
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poses a significant challenge to machine translation tools due to its precise nature and atypical use of normal words. For this reason, specialized algorithms have been developed for use in legal contexts. Due to the risk of mistranslations arising from machine translators, researchers recommend that
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with the latest advanced MT outputs. Common issues include the translation of ambiguous parts whose correct translation requires common sense-like semantic language processing or context. There can also be errors in the source texts, missing high-quality training data and the severity of frequency of
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Despite being labelled as an unworthy competitor to human translation in 1966 by the
Automated Language Processing Advisory Committee put together by the United States government, the quality of machine translation has now been improved to such levels that its application in online collaboration and
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In the early 2000s, options for machine translation between spoken and signed languages were severely limited. It was a common belief that deaf individuals could use traditional translators. However, stress, intonation, pitch, and timing are conveyed much differently in spoken languages compared to
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In addition to disambiguation problems, decreased accuracy can occur due to varying levels of training data for machine translating programs. Both example-based and statistical machine translation rely on a vast array of real example sentences as a base for translation, and when too many or too few
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There are various means for evaluating the output quality of machine translation systems. The oldest is the use of human judges to assess a translation's quality. Even though human evaluation is time-consuming, it is still the most reliable method to compare different systems such as rule-based and
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includes finding the letters in the target language that most closely correspond to the name in the source language. This, however, has been cited as sometimes worsening the quality of translation. For "Southern
California" the first word should be translated directly, while the second word should
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One of the major pitfalls of MT is its inability to translate non-standard language with the same accuracy as standard language. Heuristic or statistical based MT takes input from various sources in standard form of a language. Rule-based translation, by nature, does not include common non-standard
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Researchers continued to join the field as the
Association for Machine Translation and Computational Linguistics was formed in the U.S. (1962) and the National Academy of Sciences formed the Automatic Language Processing Advisory Committee (ALPAC) to study MT (1964). Real progress was much slower,
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of the original text with a reasonable degree of probability. It is certainly true that even purely human-generated translations are prone to error. Therefore, to ensure that a machine-generated translation will be useful to a human being and that publishable-quality translation is achieved, such
1055:, named entities, in a narrow sense, refer to concrete or abstract entities in the real world such as people, organizations, companies, and places that have a proper name: George Washington, Chicago, Microsoft. It also refers to expressions of time, space and quantity such as 1 July 2011, $ 500.
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than has yet been attained. A shallow approach which simply guessed at the sense of the ambiguous
English phrase that Piron mentions (based, perhaps, on which kind of prisoner-of-war camp is more often mentioned in a given corpus) would have a reasonable chance of guessing wrong fairly often. A
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While no system provides the ideal of fully automatic high-quality machine translation of unrestricted text, many fully automated systems produce reasonable output. The quality of machine translation is substantially improved if the domain is restricted and controlled. This enables using machine
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Interlingual machine translation was one instance of rule-based machine-translation approaches. In this approach, the source language, i.e. the text to be translated, was transformed into an interlingual language, i.e. a "language neutral" representation that is independent of any language. The
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called
Minitel. Various computer based translation companies were also launched, including Trados (1984), which was the first to develop and market Translation Memory technology (1989), though this is not the same as MT. The first commercial MT system for Russian / English / German-Ukrainian was
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Kocmi, Tom; Avramidis, Eleftherios; Bawden, Rachel; Bojar, Ondřej; Dvorkovich, Anton; Federmann, Christian; Fishel, Mark; Freitag, Markus; Gowda, Thamme; Grundkiewicz, Roman; Haddow, Barry; Koehn, Philipp; Marie, Benjamin; Monz, Christof; Morishita, Makoto (2023). Koehn, Philipp; Haddow, Barry;
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Researchers caution that the use of machine translation in medicine could risk mistranslations that can be dangerous in critical situations. Machine translation can make it easier for doctors to communicate with their patients in day to day activities, but it is recommended to only use machine
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Why does a translator need a whole workday to translate five pages, and not an hour or two? ..... About 90% of an average text corresponds to these simple conditions. But unfortunately, there's the other 10%. It's that part that requires six hours of work. There are ambiguities one has to
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articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future, especially as the MT capabilities may improve. There is a "content translation tool" which allows editors to more easily translate articles across several select languages.
980:. He pointed out that without a "universal encyclopedia", a machine would never be able to distinguish between the two meanings of a word. Today there are numerous approaches designed to overcome this problem. They can be approximately divided into "shallow" approaches and "deep" approaches.
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A third approach is a class-based model. Named entities are replaced with a token to represent their "class"; "Ted" and "Erica" would both be replaced with "person" class token. Then the statistical distribution and use of person names, in general, can be analyzed instead of looking at the
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and grammar programs. Its biggest downfall was that everything had to be made explicit: orthographical variation and erroneous input must be made part of the source language analyser in order to cope with it, and lexical selection rules must be written for all instances of ambiguity.
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in the medical field are being investigated. The application of this technology in medical settings where human translators are absent is another topic of research, but difficulties arise due to the importance of accurate translations in medical diagnoses.
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has made rapid progress in recent years. However, current consensus is that the so-called human parity achieved is not real, being based wholly on limited domains, language pairs, and certain test benchmarks i.e., it lacks statistical significance power.
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Shallow approaches assume no knowledge of the text. They simply apply statistical methods to the words surrounding the ambiguous word. Deep approaches presume a comprehensive knowledge of the word. So far, shallow approaches have been more successful.
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tools enabling mobile business networking between partners speaking different languages, or facilitating both foreign language learning and unaccompanied traveling to foreign countries without the need of the intermediation of a human translator.
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Hendy, Amr; Abdelrehim, Mohamed; Sharaf, Amr; Raunak, Vikas; Gabr, Mohamed; Matsushita, Hitokazu; Kim, Young Jin; Afify, Mohamed; Awadalla, Hany (18 February 2023). "How Good Are GPT Models at
Machine Translation? A Comprehensive Evaluation".
792:. The only interlingual machine translation system that was made operational at the commercial level was the KANT system (Nyberg and Mitamura, 1992), which was designed to translate Caterpillar Technical English (CTE) into other languages.
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Thai, Katherine; Karpinska, Marzena; Krishna, Kalpesh; Ray, Bill; Inghilleri, Moira; Wieting, John; Iyyer, Mohit (25 October 2022). "Exploring
Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature".
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There are many factors that affect how machine translation systems are evaluated. These factors include the intended use of the translation, the nature of the machine translation software, and the nature of the translation process.
1325:(EBMT), but researchers found that when evaluating English to French translation, EBMT performs better. The same concept applies for technical documents, which can be more easily translated by SMT because of their formal language.
682:
MT on the web started with SYSTRAN offering free translation of small texts (1996) and then providing this via AltaVista
Babelfish, which racked up 500,000 requests a day (1997). The second free translation service on the web was
2856:"Appendix III of 'The present status of automatic translation of languages', Advances in Computers, vol.1 (1960), p.158-163. Reprinted in Y.Bar-Hillel: Language and information (Reading, Mass.: Addison-Wesley, 1964), p.174-179"
595:) of a rudimentary translation of English into French. Several papers on the topic were published at the time, and even articles in popular journals (for example an article by Cleave and Zacharov in the September 1955 issue of
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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).
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in that it created a translation from an intermediate representation that simulated the meaning of the original sentence. Unlike interlingual MT, it depended partially on the language pair involved in the translation.
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exigencies of the target language require to be resolved. Such research is a necessary prelude to the pre-editing necessary in order to provide input for machine-translation software such that the output will not be
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languages. Within these languages, the focus is on key phrases and quick communication between military members and civilians through the use of mobile phone apps. The Information Processing Technology Office in
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usages. This causes errors in translation from a vernacular source or into colloquial language. Limitations on translation from casual speech present issues in the use of machine translation in mobile devices.
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Machine translation applications have also been released for most mobile devices, including mobile telephones, pocket PCs, PDAs, etc. Due to their portability, such instruments have come to be designated as
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shallow approach that involves "ask the user about each ambiguity" would, by Piron's estimate, only automate about 25% of a professional translator's job, leaving the harder 75% still to be done by a human.
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be transliterated. Machines often transliterate both because they treated them as one entity. Words like these are hard for machine translators, even those with a transliteration component, to process.
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are named entities, and can be further qualified via first name or other information; "president" is not, since Smith could have earlier held another position at Fabrionix, e.g. Vice President. The term
840:. Where such corpora were available, good results were achieved translating similar texts, but such corpora were rare for many language pairs. The first statistical machine translation software was
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in recent years and in low resource machine translation (when only a very limited amount of data and examples are available for training) enabled machine translation for ancient languages, such as
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rating of the translation but would change the text's human readability. They may be omitted from the output translation, which would also have implications for the text's readability and message.
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848:. In 2005, Google improved its internal translation capabilities by using approximately 200 billion words from United Nations materials to train their system; translation accuracy improved.
994:, wrote that machine translation, at its best, automates the easier part of a translator's job; the harder and more time-consuming part usually involves doing extensive research to resolve
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wrote that machine translation, at its best, automates the easier part of a translator's job; the harder and more time-consuming part usually involves doing extensive research to resolve
2016:
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Machine Translation and the Information Soup: Third Conference of the Association for Machine Translation in the Americas, AMTA'98, Langhorne, PA, USA, October 28–31, 1998 Proceedings
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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
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Antonio Toral, Sheila Castilho, Ke Hu, and Andy Way. 2018. Attaining the unattainable? reassessing claims of human parity in neural machine translation. CoRR, abs/1808.10432.
663:, which "pioneered the field under contracts from the U.S. government" in the 1960s, was used by Xerox to translate technical manuals (1978). Beginning in the late 1980s, as
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By 1998, "for as little as $ 29.95" one could "buy a program for translating in one direction between English and a major European language of your choice" to run on a PC.
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1142:. In 2012, with an aim to replace a rule-based MT by newer, statistical-based MT@EC, The European Commission contributed 3.072 million euros (via its ISA programme).
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Use of a "do-not-translate" list, which has the same end goal – transliteration as opposed to translation. still relies on correct identification of named entities.
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3781:. It is impossible however to imagine a literary-translation machine less complex than the human brain itself, with all its knowledge, reading, and discrimination."
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in 1949 is perhaps the single most influential publication in the earliest days of machine translation." Others followed. A demonstration was made in 1954 on the
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SMT's biggest downfall included it being dependent upon huge amounts of parallel texts, its problems with morphology-rich languages (especially with translating
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English-language articles are thought to usually be more comprehensive and less biased than their non-translated equivalents in other languages. As of 2022,
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Word-sense disambiguation concerns finding a suitable translation when a word can have more than one meaning. The problem was first raised in the 1950s by
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4044:. An electronic repository (and bibliography) of articles, books and papers in the field of machine translation and computer-based translation technology
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signed languages. Therefore, a deaf individual may misinterpret or become confused about the meaning of written text that is based on a spoken language.
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Named entities must first be identified in the text; if not, they may be erroneously translated as common nouns, which would most likely not affect the
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Translating and the computer 28. Proceedings of the twenty-eighth international conference on translating and the computer, 16–17 November 2006, London
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Automatic Language Processing Advisory Committee, Division of Behavioral Sciences, National Academy of Sciences, National Research Council (1966).
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671:. MT became more popular after the advent of computers. SYSTRAN's first implementation system was implemented in 1988 by the online service of the
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wrote about computer-assisted language processing as early as 1957.. was project leader on computational linguistics at Rand from 1955 to 1968.
2006:
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Researchers Zhao, et al. (2000), developed a prototype called TEAM (translation from English to ASL by machine) that completed English to
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2275:. Paper presented at the 45th Annual Meeting of the Association for Computational Linguistics, June 23–30, 2007, Prague, Czech Republic.
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protection, so some scholars claim that machine translation results are not entitled to copyright protection because MT does not involve
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1907:, which reported in 1964 as that reached the power level over the score of 90-point on the textbook of first grade of junior hi-school.)
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translation when there is no other alternative, and that translated medical texts should be reviewed by human translators for accuracy.
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2630:. Costa-jussà, Marta R., Rapp, Reinhard, Lambert, Patrik, Eberle, Kurt, Banchs, Rafael E., Babych, Bogdan. Switzerland. 21 July 2016.
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to translate a text. This approach is considered promising, but is still more resource-intensive than specialized translation models.
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translation as a tool to speed up and simplify translations, as well as producing flawed but useful low-cost or ad-hoc translations.
892:, which is thought to usually deliver the best machine translation results as of 2022, typically still need post-editing by a human.
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on the web in recent years has created yet another niche for the application of machine translation software – in utilities such as
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system in 1954. MT research programs popped up in Japan and Russia (1955), and the first MT conference was held in London (1956).
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gained popularity in Japan because of its machine translation features allowing players from different countries to communicate.
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several types of problems may not get reduced with techniques used to date, requiring some level of human active participation.
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3309:"Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases"
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wrote in 1998 that "Systran's Babelfish and GlobaLink's Comprende" handled "Don't bank on it" with a "competent performance."
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631:"wrote about computer-assisted language processing as early as 1957" and "was project leader on computational linguistics at
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560:, which are used in modern machine translation. The idea of machine translation later appeared in the 17th century. In 1629,
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3368:"Assessing the Use of Google Translate for Spanish and Chinese Translations of Emergency Department Discharge Instructions"
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Despite their inherent limitations, MT programs are used around the world. Probably the largest institutional user is the
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3472:"The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review"
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2702:. Paper presented at the 7th International EAMT Workshop on MT and Other Language Technology Tools... Archived from
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The idea of using digital computers for translation of natural languages was proposed as early as 1947 by England's
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Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012): Posters, pages 441–450
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machine translations should be reviewed by human translators for accuracy, and some courts prohibit its use in
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2303:"Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages"
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system has produced satisfactory translations that require no human intervention save for quality inspection.
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J.M. Cohen observes (p.14): "Scientific translation is the aim of an age that would reduce all activities to
2240:. Paper presented at the 8th Biennial Conference of the Association for Machine Translation in the Americas.
1895:わが国では1956年、当時の電気試験所が英和翻訳専用機「ヤマト」を実験している。この機械は1962年頃には中学1年の教科書で90点以上の能力に達したと報告されている。(translation (assisted by
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From Classroom to Real World: How Machine Translation is Changing the Landscape of Foreign Language Learning
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Gutherz, Gai; Gordin, Shai; Sáenz, Luis; Levy, Omer; Berant, Jonathan (2 May 2023). Kearns, Michael (ed.).
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For example, the Google Translate app allows foreigners to quickly translate text in their surrounding via
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Way, Andy; Nano Gough (20 September 2005). "Comparing Example-Based and Statistical Machine Translation".
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Yvette, Graham; Barry, Haddow; Koehn, Philipp (2019). "Translationese in Machine Translation Evaluation".
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MT research team, led by Professor Michael Zarechnak, followed (1951) with a public demonstration of its
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Randhawa, Gurdeeshpal; Ferreyra, Mariella; Ahmed, Rukhsana; Ezzat, Omar; Pottie, Kevin (April 2013).
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Findings of the 2023 Conference on Machine Translation (WMT23): LLMs Are Here but Not Quite There Yet
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601:). A similar application, also pioneered at Birkbeck College at the time, was reading and composing
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2506:"Study assesses the quality of AI literary translations by comparing them with human translations"
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Studies using human evaluation (e.g. by professional literary translators or human readers) have
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1204:. US Air Force has awarded a $ 1 million contract to develop a language translation technology.
3425:"Plurilingualism, multimodality and machine translation in medical consultations: A case study"
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scores for translation will result from the inclusion of methods for named entity translation.
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Weber, Steven; Mehandru, Nikita (2022). "The 2020s Political Economy of Machine Translation".
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proposed a universal language, with equivalent ideas in different tongues sharing one symbol.
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Relying exclusively on unedited machine translation ignores the fact that communication in
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using the smartphone camera that overlays the translated text onto the text. It can also
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to another, including the contextual, idiomatic and pragmatic nuances of both languages.
3615:
3506:
3471:
2391:
5299:
5019:
4495:
4475:
4199:
3938:
3400:
3367:
3366:
Khoong, Elaine C.; Steinbrook, Eric; Brown, Cortlyn; Fernandez, Alicia (1 April 2019).
3269:
1279:
The use of machine translation in law has raised concerns about translation errors and
597:
3211:(Report). Washington, D. C.: National Research Council, National Academy of Sciences.
2862:
2267:
895:
Instead of training specialized translation models on parallel datasets, one can also
5366:
5331:
5158:
4758:
4570:
4550:
4331:
3999:
3456:
3352:
2417:
2302:
1853:
1828:
1459:
1349:
1268:
1236:, etc. – allowing users speaking different languages to communicate with each other.
1233:
877:
720:
628:
580:
572:
545:
541:
278:
181:
4845:
3383:
2589:
Milestones in machine translation – No.6: Bar-Hillel and the nonfeasibility of FAHQT
2269:
Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora
2142:"Babel Fish: What Happened To The Original Translation Application?: We Investigate"
915:
47:
5235:
5066:
5034:
4738:
3676:
3662:
2676:
1919:
1373:
1046:
987:
750:
3325:
3308:
2729:
Name Translation in Statistical Machine Translation Learning When to Transliterate
17:
3677:
Fully Automatic High Quality Machine Translation of Restricted Text: A Case Study
3205:
2608:
810:
entries, which means that the words were translated as they are by a dictionary.
5230:
5225:
5046:
4695:
4575:
4288:
4204:
4181:
4129:
3755:
LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors
3598:
1630:
1610:
1605:
1500:
1479:
1463:
1385:
1381:
1229:
1011:
1003:
999:
859:
664:
553:
491:
257:
252:
95:
67:
2574:
2400:
2375:
2232:
749:
The rule-based machine translation approach was used mostly in the creation of
5190:
4298:
4084:
4051:
3851:
3778:
3654:
3044:"Content translation tool helps create over half a million Knowledge articles"
1487:
1471:
1071:
is what defines these usages for analysis in statistical machine translation.
807:
557:
406:
3606:
3497:
3448:
3391:
3344:
2696:
Improving Machine Translation Quality with Automatic Named Entity Recognition
2645:
2607:
Bar-Hillel (1960), "Automatic Translation of Languages". Available online at
1805:. Studies in the History of the Language Sciences. Vol. 97. p. 17.
1167:
each only have over 2.5 million articles, each often far less comprehensive.
5220:
4166:
4075:
Slator News & analysis of the latest developments in machine translation
1810:
1600:
1467:
1424:
1377:
1244:
1151:
995:
919:
Machine translation could produce some non-understandable phrases, such as "
732:
247:
3624:
3515:
3409:
3278:
2788:
Melby, Alan. The Possibility of Language (Amsterdam:Benjamins, 1995, 27–41)
2409:
1725:"The Cryptological Origins of Machine Translation: From al-Kindi to Weaver"
4074:
3440:
3307:
Vieira, Lucas Nunes; O’Hagan, Minako; O’Sullivan, Carol (18 August 2021).
3252:
1947:"Speaking in Tongues: Science's centuries-long hunt for a common language"
1698:"Google Translate vs. ChatGPT: Which One Is the Best Language Translator?"
1328:
In certain applications, however, e.g., product descriptions written in a
5141:
5116:
4641:
4621:
4606:
4585:
4555:
4500:
4465:
4346:
1418:
1413:
1341:
1217:
537:
495:
4934:
3991:
3867:
Zhao, L., Kipper, K., Schuler, W., Vogler, C., & Palmer, M. (2000).
3791:
2559:
2141:
4778:
4636:
4616:
4490:
4234:
4149:
2568:. Singapore: Association for Computational Linguistics. pp. 1–42.
1409:
829:
660:
602:
242:
3534:"Man v. Machine: Social and Legal Implications of Machine Translation"
2329:
1982:
Scientific Babel: How Science Was Done Before and After Global English
1317:
Different programs may work well for different purposes. For example,
667:
power increased and became less expensive, more interest was shown in
544:
who developed techniques for systemic language translation, including
4144:
4139:
2941:"Google Translate app update said to make speech-to-text even easier"
1483:
1353:
824:
Statistical machine translation tried to generate translations using
728:
536:
The origins of machine translation can be traced back to the work of
401:
396:
723:
methods, statistical methods required a lot of rules accompanied by
3982:
3869:
A Machine Translation System from English to American Sign Language
2542:
2489:
2359:
1179:, the U.S. and its allies have been most interested in developing
701:
translates roughly enough text to fill 1 million books in one day.
5136:
4834:
4470:
3488:
3180:"Korean Games Growing in Popularity in Tough Japanese Game Market"
2319:
1357:
1225:
1193:
934:
914:
645:
584:
46:
3583:"Translating Akkadian to English with neural machine translation"
3095:
1367:
is context-embedded and that it takes a person to comprehend the
832:
corpus, the English-French record of the Canadian parliament and
4356:
4047:
1901:
National Institute of Advanced Industrial Science and Technology
1486:
when a work is translated: a translator must have permission to
1176:
855:
such languages), and its inability to correct singleton errors.
4938:
4849:
4088:
4018:
3470:
Herrera-Espejel, Paula Sofia; Rach, Stefan (20 November 2023).
3206:
Language and Machines: Computers in Translation and Linguistics
2887:
2376:"Poor English skills? New AIs help researchers to write better"
4631:
2007:"David G. Hays, 66, a Developer Of Language Study by Computer"
1903:(AIST) tested the proper English-Japanese translation machine
1372:
translations must be reviewed and edited by a human. The late
845:
4030:
2914:"Google Translate Adds 20 Languages To Augmented Reality App"
2754:
Using Named Entity Recognition to improve Machine Translation
2115:
Farwell, David; Gerber, Laurie; Hovy, Eduard (29 June 2003).
1796:
J. Hutchins (2000). "Warren Weaver and the launching of MT".
1175:
Following terrorist attacks in Western countries, including
858:
Some work has been done in the utilization of multiparallel
2888:"Human quality machine translation solution by Ta with you"
2566:
Proceedings of the Eighth Conference on Machine Translation
2073:
Budiansky, Stephen (December 1998). "Lost in Translation".
1756:
Universal Language Schemes in England and France, 1600-1800
1058:
In the sentence "Smith is the president of Fabrionix" both
1024:
3018:"Knowledge taps Google to help editors translate articles"
3735:
Machine translation as a tool in second language learning
2433:"DeepL: An Exceptionally Magnificent Language Translator"
1779:
Booth, Andrew D. (1 May 1953). "MECHANICAL TRANSLATION".
4019:
International Association for Machine Translation (IAMT)
3709:"Comparison of MT systems by human evaluation, May 2008"
3071:"Knowledge Has a Language Problem. Here's How To Fix It"
990:, a long-time translator for the United Nations and the
51:
A mobile phone app translating Spanish text into English
4014:
The Advantages and Disadvantages of Machine Translation
3560:"New Mexico's Success with Non-English Speaking Jurors"
3153:"GCN – Air force wants to build a universal translator"
2738:. Association for Computational Linguistics. 389–397.
2068:
2066:
2064:
2062:
1150:
Machine translation has also been used for translating
3959:
Lewis-Kraus, Gideon (7 June 2015). "Tower of Babble".
2458:"DeepL outperforms Google Translate – DW – 12/05/2018"
1516:
Comparison of different machine translation approaches
1586:
List of research laboratories for machine translation
2683:(The Language Challenge), Paris, L'Harmattan, 1994.
5183:
4979:
4972:
4812:
4767:
4722:
4694:
4654:
4599:
4521:
4509:
4440:
4397:
4369:
4317:
4180:
4122:
3816:"4 times Google Translate totally dropped the ball"
2727:Hermajakob, U., Knight, K., & Hal, D. (2008).
3937:
3889:"Machine Translation: No Copyright On The Result?"
1984:. Chicago, Illinois: University of Chicago Press.
768:Transfer-based machine translation was similar to
27:Computerized translation between natural languages
3878:. Lecture Notes in Computer Science, 1934: 54–67.
3834:"回数を重ねるほど狂っていく Google翻訳で「えぐ」を英訳すると奇妙な世界に迷い込むと話題に"
2826:"A Simple Model Outlining Translation Technology"
2530:
2528:
2526:
1404:Flaws in machine translation have been noted for
4048:Machine translation (computer-based translation)
3863:
3861:
3253:"Using machine translation in clinical practice"
2182:Routledge Encyclopedia of Translation Technology
1672:"Google Translate Gets a Deep-Learning Upgrade"
1016:
3636:
3634:
2609:http://www.mt-archive.info/Bar-Hillel-1960.pdf
1550:Fuzzy matching (computer-assisted translation)
1511:Comparison of machine translation applications
788:target language was then generated out of the
676:developed at Kharkov State University (1991).
509:. These methods have since been superseded by
4950:
4861:
4100:
1782:Computers and Automation 1953-05: Vol 2 Iss 4
940:
920:
828:based on bilingual text corpora, such as the
579:in the same year. "The memorandum written by
468:
8:
3236:: CS1 maint: multiple names: authors list (
2045:(in Japanese). Tokyo: (株)ラッセル社. p. 16.
1883:(in Japanese). Tokyo: (株)ラッセル社. p. 16.
2307:Journal of Artificial Intelligence Research
2206:"Google Translator: The Universal Language"
1482:in the original language does not lose his
806:Machine translation used a method based on
5345:
4976:
4957:
4943:
4935:
4868:
4854:
4846:
4518:
4314:
4107:
4093:
4085:
4058:Machine Translation and Minority Languages
4050:– Publications by John Hutchins (includes
2993:"Knowledge has a Google Translate problem"
2658:: CS1 maint: location missing publisher (
1535:Controlled language in machine translation
1299:and its dialects Babylonian and Assyrian.
669:statistical models for machine translation
475:
461:
54:
3981:
3614:
3505:
3487:
3399:
3336:1983/29727bd1-a1ae-4600-9e8e-018f11ec75fb
3334:
3324:
3268:
2890:(in Spanish). Tauyou.com. 15 April 2009.
2746:
2744:
2693:Babych, Bogdan; Hartley, Anthony (2003).
2573:
2541:
2488:
2399:
2358:
2328:
2318:
2208:. Blog.outer-court.com. 25 January 2007.
4054:of several books on machine translation)
3792:annually performed NIST tests since 2001
3313:Information, Communication & Society
2628:Hybrid approaches to machine translation
1758:. Toronto: University of Toronto Press.
1432:Machine translation and signed languages
1159:has over 6.5 million articles while the
955:systematically identified various issues
5378:Applications of artificial intelligence
5052:Carbon nanotube field-effect transistor
5010:Applications of artificial intelligence
1854:"Warren Weaver, American mathematician"
1663:
1571:Language and Communication Technologies
347:
316:
270:
214:
168:
82:
66:
5201:Differential technological development
3940:An Introduction to Machine Translation
3302:
3300:
3229:
3122:"Machine Translation for the Military"
2894:from the original on 22 September 2009
2651:
2266:Cohn, Trevor; Lapata, Mirella (2007).
1631:Translation § Machine translation
617:, began his research at MIT (1951). A
490:is use of computational techniques to
427:Bhagavad-gita translations by language
3899:from the original on 29 November 2012
3527:
3525:
3151:Jackson, William (9 September 2003).
2972:from the original on 8 September 2013
2301:Nakov, Preslav; Ng, Hwee Tou (2012).
2212:from the original on 20 November 2008
2152:from the original on 20 November 2019
1438:Machine translation of sign languages
888:Translations by neural MT tools like
284:Internationalization and localization
7:
5393:Tasks of natural language processing
5171:Three-dimensional integrated circuit
4566:Simple Knowledge Organization System
3922:Cohen, J. M. (1986), "Translation",
3429:Translation and Interpreting Studies
3218:from the original on 21 October 2013
2282:from the original on 10 October 2015
2019:from the original on 7 February 2020
1926:from the original on 19 October 2016
1334:dictionary-based machine-translation
802:Dictionary-based machine translation
5290:Future-oriented technology analysis
5030:Progress in artificial intelligence
3476:JMIR Public Health and Surveillance
3069:Magazine, Undark (12 August 2021).
2558:Kocmi, Tom; Monz, Christof (eds.).
2374:Katsnelson, Alla (29 August 2022).
2140:Barron, Brenda (18 November 2019).
1412:in April 2017 involve two Japanese
613:The first researcher in the field,
1961:from the original on 3 August 2020
1799:Early Years in Machine Translation
1785:. Berkeley Enterprises. p. 6.
1641:ULTRA (machine translation system)
1474:. The copyright at issue is for a
1183:programs, but also in translating
764:Transfer-based machine translation
758:Transfer-based machine translation
417:Books and magazines on translation
25:
4581:Thesaurus (information retrieval)
3159:from the original on 16 June 2011
3120:Gallafent, Alex (26 April 2011).
2824:Wooten, Adam (14 February 2006).
2247:from the original on 29 June 2016
2119:. Berlin: Springer. p. 276.
2094:. New York: Elsevier. p. 5.
2092:Conceptual Information Processing
1860:from the original on 6 March 2021
1323:example-based machine translation
1309:Evaluation of machine translation
715:Example-based machine translation
5344:
3711:. Morphologic.hu. Archived from
3423:Piccoli, Vanessa (5 July 2022).
3178:Young-sil, Yoon (26 June 2023).
2805:from the original on 25 May 2011
2767:from the original on 21 May 2013
2431:Korab, Petr (18 February 2022).
2234:Multi-Source Translation Methods
2184:. Oxon: Routledge. p. 385.
1838:on 28 February 2020 – via
931:) being rendered as "wikipedia".
783:Interlingual machine translation
770:interlingual machine translation
74:
5067:Fourth-generation optical discs
3803:Bilingual Evaluation Understudy
3744:. CALICO Journal. 13(1). 68–96.
3384:10.1001/jamainternmed.2018.7653
3285:from the original on 4 May 2013
3132:from the original on 9 May 2013
2751:Neeraj Agrawal; Ankush Singla.
2005:Wolfgang Saxon (28 July 1995).
1626:Statistical machine translation
1319:statistical machine translation
820:Statistical machine translation
36:Interactive machine translation
4162:Natural language understanding
3928:, vol. 27, pp. 12–15
1723:DuPont, Quinn (January 2018).
1555:History of machine translation
945:" from machine translation in
745:Rule-based machine translation
527:History of machine translation
449:Kural translations by language
422:Bible translations by language
197:Dynamic and formal equivalence
1:
5388:Computer-assisted translation
5317:Technology in science fiction
4686:Optical character recognition
3326:10.1080/1369118X.2020.1776370
2966:"Machine Translation Service"
1646:Universal Networking Language
1581:List of emerging technologies
1526:Computer-assisted translation
1293:convolutional neural networks
501:Early approaches were mostly
437:List of most translated works
238:Translation management system
32:Computer-assisted translation
4379:Multi-document summarization
3936:; Somers, Harold L. (1992).
3854:– via www.youtube.com.
3643:Natural Language Engineering
1545:Foreign language writing aid
1344:means of evaluation include
1321:(SMT) typically outperforms
4709:Latent Dirichlet allocation
4681:Natural language generation
4546:Machine-readable dictionary
4541:Linguistic Linked Open Data
4116:Natural language processing
4031:Machine Translation Archive
3564:Journal of Court Innovation
3096:"List of Wikipedias - Meta"
2991:Wilson, Kyle (8 May 2019).
2170:and gave other examples too
1980:Gordin, Michael D. (2015).
1540:Controlled natural language
5414:
5322:Technology readiness level
5258:Technological unemployment
4461:Explicit semantic analysis
4210:Deep linguistic processing
3944:. London: Academic Press.
3558:Chavez, Edward L. (2008).
2401:10.1038/d41586-022-02767-9
2043:パーソナルコンピュータによる機械翻訳プログラムの制作
1881:パーソナルコンピュータによる機械翻訳プログラムの制作
1596:Neural machine translation
1435:
1306:
1181:Arabic machine translation
1044:
965:
882:neural machine translation
872:Neural machine translation
869:
817:
799:
780:
761:
742:
711:Hybrid machine translation
708:
524:
511:neural machine translation
29:
5383:Computational linguistics
5340:
5305:Technological singularity
5265:Technological convergence
4884:
4304:Word-sense disambiguation
4157:Computational linguistics
3891:. SEO Translator, citing
3655:10.1017/S1351324905003888
3599:10.1093/pnasnexus/pgad096
3532:legalj (2 January 2023).
3257:Canadian Family Physician
2614:28 September 2011 at the
2090:Schank, Roger C. (2014).
2041:上野, 俊夫 (13 August 1986).
1879:上野, 俊夫 (13 August 1986).
1560:Human language technology
1521:Computational linguistics
1408:. Two videos uploaded to
1406:their entertainment value
1171:Surveillance and military
992:World Health Organization
968:Word-sense disambiguation
941:
921:
623:Georgetown-IBM experiment
540:, a ninth-century Arabic
5077:Holographic data storage
4830:Natural Language Toolkit
4754:Pronunciation assessment
4656:Automatic identification
4486:Latent semantic analysis
4442:Distributional semantics
4327:Compound-term processing
4225:Named-entity recognition
4068:7 September 2007 at the
2575:10.18653/v1/2023.wmt-1.1
1920:"機械翻訳専用機「やまと」-コンピュータ博物館"
1754:Knowlson, James (1975).
1014:require to be resolved:
972:Syntactic disambiguation
494:text or speech from one
412:Journalistic translation
30:Not to be confused with
5270:Technological evolution
5243:Exploratory engineering
5072:3D optical data storage
5005:Artificial intelligence
4734:Automated essay scoring
4704:Document classification
4371:Automatic summarization
3961:New York Times Magazine
3733:Anderson, D.D. (1995).
3682:17 October 2011 at the
3538:Princeton Legal Journal
2791:. Benjamins.com. 1995.
2231:Schwartz, Lane (2008).
1811:10.1075/sihols.97.05hut
1130:and then translate it.
928:Macrolepiota albuminosa
880:-based approach to MT,
644:however, and after the
294:Video game localization
202:Contrastive linguistics
5280:Technology forecasting
5275:Technological paradigm
5248:Proactionary principle
5166:Software-defined radio
4591:Universal Dependencies
4284:Terminology extraction
4267:Semantic decomposition
4262:Semantic role labeling
4252:Part-of-speech tagging
4220:Information extraction
4205:Coreference resolution
4195:Collocation extraction
3925:Encyclopedia Americana
3760:4 January 2018 at the
3740:4 January 2018 at the
3372:JAMA Internal Medicine
2734:4 January 2018 at the
2180:Chan, Sin-Wai (2015).
1899:): In 1959 Japan, the
1621:Round-trip translation
1448:American Sign Language
1281:client confidentiality
1053:information extraction
1021:
950:
932:
577:Rockefeller Foundation
377:Telephone interpreting
263:Multimedia translation
52:
40:Translator (computing)
5206:Disruptive innovation
4966:Emerging technologies
4352:Sentence segmentation
3970:Business and Politics
3797:22 March 2009 at the
3441:10.1075/tis.21012.pic
2662:) CS1 maint: others (
2594:12 March 2007 at the
1945:Nye, Mary Jo (2016).
1565:Humour in translation
1340:statistical systems.
1196:hosted programs like
1134:Public administration
938:
918:
901:large language models
719:Before the advent of
685:Lernout & Hauspie
673:French Postal Service
619:Georgetown University
515:large language models
309:Software localization
289:Language localization
192:Translation criticism
121:Linguistic validation
50:
5253:Technological change
5196:Collingridge dilemma
4993:Ambient intelligence
4804:Voice user interface
4515:datasets and corpora
4456:Document-term matrix
4309:Word-sense induction
4036:1 April 2019 at the
4024:24 June 2010 at the
3893:Zimbabwe Independent
3874:20 July 2018 at the
3753:Han et al. (2012), "
3048:Wikimedia Foundation
2868:on 28 September 2018
1651:Universal translator
1506:Cache language model
1478:; the author of the
1291:The advancements in
1212:The notable rise of
836:, the record of the
635:from 1955 to 1968."
593:University of London
304:Website localization
5398:Automation software
5373:Machine translation
5310:Technology scouting
5285:Accelerating change
5015:Machine translation
4878:machine translation
4784:Interactive fiction
4714:Pachinko allocation
4671:Speech segmentation
4627:Google Ngram Viewer
4399:Machine translation
4389:Text simplification
4384:Sentence extraction
4272:Semantic similarity
3992:10.1017/bap.2021.17
3050:. 23 September 2019
2681:Le défi des langues
2392:2022Natur.609..208K
1330:controlled language
1140:European Commission
1032:Non-standard speech
978:Yehoshua Bar-Hillel
838:European Parliament
826:statistical methods
615:Yehoshua Bar-Hillel
605:texts by computer.
488:Machine translation
371:Video relay service
228:Machine translation
187:Translation project
177:Translation studies
5327:Technology roadmap
5040:Speech recognition
5025:Mobile translation
4998:Internet of things
4794:Question answering
4666:Speech recognition
4531:Corpus linguistics
4511:Language resources
4294:Textual entailment
4277:Sentiment analysis
4063:John Hutchins 1999
3100:meta.wikimedia.org
2012:The New York Times
1704:. 23 February 2024
1636:Translation memory
1616:Pseudo-translation
1591:Mobile translation
1530:Translation memory
1274:formal proceedings
1202:Babylon translator
1165:Swedish Wikipedias
1116:mobile translation
1010:exigencies of the
951:
933:
550:frequency analysis
233:Mobile translation
53:
18:Machine translator
5360:
5359:
5179:
5178:
5149:Optical computing
4932:
4931:
4843:
4842:
4799:Virtual assistant
4724:Computer-assisted
4650:
4649:
4407:Computer-assisted
4365:
4364:
4357:Word segmentation
4319:Text segmentation
4257:Semantic analysis
4245:Syntactic parsing
4230:Ontology learning
3963:. pp. 48–52.
3934:Hutchins, W. John
3696:978-0-85142-483-5
3690:, London: Aslib.
3319:(11): 1515–1532.
2968:. 5 August 2011.
2504:Fadelli, Ingrid.
2386:(7925): 208–209.
2330:10.1613/jair.3540
2079:. pp. 81–84.
2076:Atlantic Magazine
1820:978-90-272-4586-1
1735:on 14 August 2019
1287:Ancient languages
1222:instant messaging
1214:social networking
1157:English Knowledge
1124:augmented reality
689:Atlantic Magazine
485:
484:
356:Untranslatability
207:Polysystem theory
16:(Redirected from
5405:
5348:
5347:
5295:Horizon scanning
5211:Ephemeralization
5127:Racetrack memory
5062:Extended reality
5057:Cybermethodology
4977:
4959:
4952:
4945:
4936:
4889:Dictionary-based
4870:
4863:
4856:
4847:
4820:Formal semantics
4769:Natural language
4676:Speech synthesis
4658:and data capture
4561:Semantic network
4536:Lexical resource
4519:
4337:Lexical analysis
4315:
4240:Semantic parsing
4109:
4102:
4095:
4086:
4003:
3985:
3964:
3955:
3943:
3929:
3909:
3908:
3906:
3904:
3885:
3879:
3865:
3856:
3855:
3848:
3842:
3841:
3830:
3824:
3823:
3820:Business Insider
3811:
3805:
3788:
3782:
3775:
3769:
3768:, Mumbai, India.
3751:
3745:
3731:
3725:
3724:
3722:
3720:
3715:on 19 April 2012
3705:
3699:
3675:Muegge (2006), "
3673:
3667:
3666:
3638:
3629:
3628:
3618:
3578:
3572:
3571:
3555:
3549:
3548:
3546:
3544:
3529:
3520:
3519:
3509:
3491:
3467:
3461:
3460:
3420:
3414:
3413:
3403:
3363:
3357:
3356:
3338:
3328:
3304:
3295:
3294:
3292:
3290:
3272:
3248:
3242:
3241:
3235:
3227:
3225:
3223:
3217:
3210:
3201:
3195:
3194:
3192:
3190:
3175:
3169:
3168:
3166:
3164:
3148:
3142:
3141:
3139:
3137:
3117:
3111:
3110:
3108:
3106:
3092:
3086:
3085:
3083:
3081:
3066:
3060:
3059:
3057:
3055:
3040:
3034:
3033:
3031:
3029:
3024:. 9 January 2019
3014:
3008:
3007:
3005:
3003:
2988:
2982:
2981:
2979:
2977:
2962:
2956:
2955:
2953:
2951:
2939:Whitney, Lance.
2936:
2930:
2929:
2927:
2925:
2910:
2904:
2903:
2901:
2899:
2884:
2878:
2877:
2875:
2873:
2867:
2861:. Archived from
2860:
2852:
2846:
2845:
2843:
2841:
2832:. Archived from
2830:T&I Business
2821:
2815:
2814:
2812:
2810:
2783:
2777:
2776:
2774:
2772:
2766:
2759:
2748:
2739:
2725:
2719:
2718:
2716:
2714:
2708:
2701:
2690:
2684:
2674:
2668:
2667:
2657:
2649:
2624:
2618:
2605:
2599:
2598:by John Hutchins
2586:
2580:
2579:
2577:
2554:
2548:
2547:
2545:
2532:
2521:
2520:
2518:
2516:
2501:
2495:
2494:
2492:
2479:
2473:
2472:
2470:
2468:
2454:
2448:
2447:
2445:
2443:
2428:
2422:
2421:
2403:
2371:
2365:
2364:
2362:
2350:
2344:
2341:
2335:
2334:
2332:
2322:
2298:
2292:
2291:
2289:
2287:
2281:
2274:
2263:
2257:
2256:
2254:
2252:
2246:
2239:
2228:
2222:
2221:
2219:
2217:
2202:
2196:
2195:
2177:
2171:
2168:
2162:
2161:
2159:
2157:
2137:
2131:
2130:
2112:
2106:
2105:
2087:
2081:
2080:
2070:
2057:
2056:
2038:
2032:
2031:
2026:
2024:
2002:
1996:
1995:
1977:
1971:
1970:
1968:
1966:
1942:
1936:
1935:
1933:
1931:
1916:
1910:
1909:
1897:Google Translate
1876:
1870:
1869:
1867:
1865:
1856:. 13 July 2020.
1850:
1844:
1843:
1840:Semantic Scholar
1837:
1831:. Archived from
1804:
1793:
1787:
1786:
1776:
1770:
1769:
1751:
1745:
1744:
1742:
1740:
1731:. Archived from
1720:
1714:
1713:
1711:
1709:
1694:
1688:
1687:
1685:
1683:
1678:. 3 October 2016
1668:
1576:Language barrier
1224:clients such as
1128:recognize speech
1069:rigid designator
944:
943:
939:Broken Chinese "
924:
923:
890:DeepL Translator
830:Canadian Hansard
796:Dictionary-based
699:Google Translate
589:Birkbeck College
477:
470:
463:
432:Translated books
382:Language barrier
299:Dub localization
78:
55:
21:
5413:
5412:
5408:
5407:
5406:
5404:
5403:
5402:
5363:
5362:
5361:
5356:
5336:
5175:
4986:
4983:
4982:Information and
4968:
4963:
4933:
4928:
4880:
4874:
4844:
4839:
4808:
4788:Syntax guessing
4770:
4763:
4749:Predictive text
4744:Grammar checker
4725:
4718:
4690:
4657:
4646:
4612:Bank of English
4595:
4523:
4514:
4505:
4436:
4393:
4361:
4313:
4215:Distant reading
4190:Argument mining
4176:
4172:Text processing
4118:
4113:
4070:Wayback Machine
4038:Wayback Machine
4026:Wayback Machine
4010:
3967:
3958:
3952:
3932:
3921:
3918:
3916:Further reading
3913:
3912:
3902:
3900:
3887:
3886:
3882:
3876:Wayback Machine
3866:
3859:
3850:
3849:
3845:
3832:
3831:
3827:
3813:
3812:
3808:
3799:Wayback Machine
3789:
3785:
3776:
3772:
3762:Wayback Machine
3752:
3748:
3742:Wayback Machine
3732:
3728:
3718:
3716:
3707:
3706:
3702:
3684:Wayback Machine
3674:
3670:
3640:
3639:
3632:
3580:
3579:
3575:
3557:
3556:
3552:
3542:
3540:
3531:
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3523:
3469:
3468:
3464:
3422:
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3417:
3365:
3364:
3360:
3306:
3305:
3298:
3288:
3286:
3250:
3249:
3245:
3228:
3221:
3219:
3215:
3208:
3203:
3202:
3198:
3188:
3186:
3177:
3176:
3172:
3162:
3160:
3150:
3149:
3145:
3135:
3133:
3126:PRI's the World
3119:
3118:
3114:
3104:
3102:
3094:
3093:
3089:
3079:
3077:
3075:Undark Magazine
3068:
3067:
3063:
3053:
3051:
3042:
3041:
3037:
3027:
3025:
3016:
3015:
3011:
3001:
2999:
2990:
2989:
2985:
2975:
2973:
2964:
2963:
2959:
2949:
2947:
2938:
2937:
2933:
2923:
2921:
2918:Popular Science
2912:
2911:
2907:
2897:
2895:
2886:
2885:
2881:
2871:
2869:
2865:
2858:
2854:
2853:
2849:
2839:
2837:
2836:on 16 July 2012
2823:
2822:
2818:
2808:
2806:
2799:
2785:
2784:
2780:
2770:
2768:
2764:
2757:
2750:
2749:
2742:
2736:Wayback Machine
2726:
2722:
2712:
2710:
2706:
2699:
2692:
2691:
2687:
2675:
2671:
2650:
2638:
2626:
2625:
2621:
2616:Wayback Machine
2606:
2602:
2596:Wayback Machine
2587:
2583:
2556:
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2551:
2534:
2533:
2524:
2514:
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2503:
2502:
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2300:
2299:
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2279:
2272:
2265:
2264:
2260:
2250:
2248:
2244:
2237:
2230:
2229:
2225:
2215:
2213:
2204:
2203:
2199:
2192:
2179:
2178:
2174:
2169:
2165:
2155:
2153:
2139:
2138:
2134:
2127:
2114:
2113:
2109:
2102:
2089:
2088:
2084:
2072:
2071:
2060:
2053:
2040:
2039:
2035:
2022:
2020:
2004:
2003:
1999:
1992:
1979:
1978:
1974:
1964:
1962:
1944:
1943:
1939:
1929:
1927:
1918:
1917:
1913:
1891:
1878:
1877:
1873:
1863:
1861:
1852:
1851:
1847:
1835:
1821:
1802:
1795:
1794:
1790:
1778:
1777:
1773:
1766:
1753:
1752:
1748:
1738:
1736:
1722:
1721:
1717:
1707:
1705:
1696:
1695:
1691:
1681:
1679:
1670:
1669:
1665:
1660:
1655:
1496:
1490:a translation.
1476:derivative work
1466:are subject to
1456:
1440:
1434:
1416:characters えぐ (
1311:
1305:
1289:
1266:
1253:
1242:
1210:
1173:
1148:
1136:
1111:
1102:
1082:Transliteration
1049:
1043:
1034:
1012:target language
974:
966:Main articles:
964:
947:Bali, Indonesia
913:
897:directly prompt
874:
868:
822:
816:
804:
798:
785:
779:
766:
760:
747:
741:
717:
707:
695:Franz Josef Och
658:
656:1975 and beyond
641:
611:
534:
529:
523:
481:
387:Fan translation
366:Transliteration
156:Sense-for-sense
43:
28:
23:
22:
15:
12:
11:
5:
5411:
5409:
5401:
5400:
5395:
5390:
5385:
5380:
5375:
5365:
5364:
5358:
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5341:
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5146:
5145:
5144:
5139:
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5129:
5124:
5119:
5114:
5109:
5104:
5099:
5094:
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5081:
5080:
5079:
5074:
5064:
5059:
5054:
5049:
5044:
5043:
5042:
5037:
5032:
5027:
5022:
5020:Machine vision
5017:
5012:
5002:
5001:
5000:
4989:
4987:
4984:communications
4980:
4974:
4970:
4969:
4964:
4962:
4961:
4954:
4947:
4939:
4930:
4929:
4927:
4926:
4921:
4916:
4911:
4906:
4901:
4899:Transfer-based
4896:
4891:
4885:
4882:
4881:
4876:Approaches to
4875:
4873:
4872:
4865:
4858:
4850:
4841:
4840:
4838:
4837:
4832:
4827:
4822:
4816:
4814:
4810:
4809:
4807:
4806:
4801:
4796:
4791:
4781:
4775:
4773:
4771:user interface
4765:
4764:
4762:
4761:
4756:
4751:
4746:
4741:
4736:
4730:
4728:
4720:
4719:
4717:
4716:
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4700:
4698:
4692:
4691:
4689:
4688:
4683:
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4673:
4668:
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4660:
4652:
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4648:
4647:
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4644:
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4619:
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4603:
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4593:
4588:
4583:
4578:
4573:
4568:
4563:
4558:
4553:
4548:
4543:
4538:
4533:
4527:
4525:
4516:
4507:
4506:
4504:
4503:
4498:
4496:Word embedding
4493:
4488:
4483:
4476:Language model
4473:
4468:
4463:
4458:
4453:
4447:
4445:
4438:
4437:
4435:
4434:
4429:
4427:Transfer-based
4424:
4419:
4414:
4409:
4403:
4401:
4395:
4394:
4392:
4391:
4386:
4381:
4375:
4373:
4367:
4366:
4363:
4362:
4360:
4359:
4354:
4349:
4344:
4339:
4334:
4329:
4323:
4321:
4312:
4311:
4306:
4301:
4296:
4291:
4286:
4280:
4279:
4274:
4269:
4264:
4259:
4254:
4249:
4248:
4247:
4242:
4232:
4227:
4222:
4217:
4212:
4207:
4202:
4200:Concept mining
4197:
4192:
4186:
4184:
4178:
4177:
4175:
4174:
4169:
4164:
4159:
4154:
4153:
4152:
4147:
4137:
4132:
4126:
4124:
4120:
4119:
4114:
4112:
4111:
4104:
4097:
4089:
4083:
4082:
4077:
4072:
4060:
4055:
4045:
4028:
4016:
4009:
4008:External links
4006:
4005:
4004:
3965:
3956:
3950:
3930:
3917:
3914:
3911:
3910:
3880:
3857:
3843:
3825:
3806:
3783:
3770:
3746:
3726:
3700:
3668:
3649:(3): 295–309.
3630:
3593:(5): pgad096.
3573:
3550:
3521:
3462:
3415:
3378:(4): 580–582.
3358:
3296:
3263:(4): 382–383.
3243:
3196:
3170:
3143:
3112:
3087:
3061:
3035:
3009:
2983:
2957:
2931:
2920:. 30 July 2015
2905:
2879:
2847:
2816:
2797:
2778:
2740:
2720:
2709:on 14 May 2006
2685:
2669:
2636:
2619:
2600:
2581:
2549:
2522:
2510:techxplore.com
2496:
2474:
2462:Deutsche Welle
2449:
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2132:
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1937:
1911:
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1532:
1523:
1518:
1513:
1508:
1503:
1497:
1495:
1492:
1455:
1452:
1436:Main article:
1433:
1430:
1365:human language
1307:Main article:
1304:
1301:
1288:
1285:
1269:Legal language
1265:
1262:
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1249:
1241:
1238:
1209:
1206:
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1169:
1147:
1144:
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1101:
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1045:Main article:
1042:
1041:Named entities
1039:
1033:
1030:
963:
962:Disambiguation
960:
912:
909:
870:Main article:
867:
864:
818:Main article:
815:
812:
800:Main article:
797:
794:
781:Main article:
778:
775:
762:Main article:
759:
756:
743:Main article:
740:
737:
706:
703:
687:'s GlobaLink.
657:
654:
640:
637:
610:
607:
598:Wireless World
562:René Descartes
533:
530:
525:Main article:
522:
519:
483:
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472:
465:
457:
454:
453:
452:
451:
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441:
440:
439:
429:
424:
419:
414:
409:
404:
399:
394:
391:of video games
384:
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349:
348:Related topics
345:
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337:
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153:
148:
143:
141:Interpretation
138:
133:
128:
123:
118:
113:
108:
103:
98:
93:
85:
84:
80:
79:
71:
70:
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63:
26:
24:
14:
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10:
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4:
3:
2:
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5376:
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5371:
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5368:
5353:
5352:
5343:
5342:
5339:
5333:
5332:Transhumanism
5330:
5328:
5325:
5323:
5320:
5318:
5315:
5311:
5308:
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5197:
5194:
5192:
5189:
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5167:
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5160:
5159:Chipless RFID
5157:
5156:
5155:
5152:
5150:
5147:
5143:
5140:
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5133:
5130:
5128:
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4955:
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4948:
4946:
4941:
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4937:
4925:
4922:
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4915:
4912:
4910:
4909:Example-based
4907:
4905:
4902:
4900:
4897:
4895:
4892:
4890:
4887:
4886:
4883:
4879:
4871:
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4864:
4859:
4857:
4852:
4851:
4848:
4836:
4833:
4831:
4828:
4826:
4825:Hallucination
4823:
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4815:
4811:
4805:
4802:
4800:
4797:
4795:
4792:
4789:
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4777:
4776:
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4766:
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4759:Spell checker
4757:
4755:
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4707:
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4598:
4592:
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4582:
4579:
4577:
4574:
4572:
4571:Speech corpus
4569:
4567:
4564:
4562:
4559:
4557:
4554:
4552:
4551:Parallel text
4549:
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4443:
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4433:
4430:
4428:
4425:
4423:
4420:
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4413:
4412:Example-based
4410:
4408:
4405:
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4402:
4400:
4396:
4390:
4387:
4385:
4382:
4380:
4377:
4376:
4374:
4372:
4368:
4358:
4355:
4353:
4350:
4348:
4345:
4343:
4342:Text chunking
4340:
4338:
4335:
4333:
4332:Lemmatisation
4330:
4328:
4325:
4324:
4322:
4320:
4316:
4310:
4307:
4305:
4302:
4300:
4297:
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4260:
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4208:
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4187:
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4183:
4182:Text analysis
4179:
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4148:
4146:
4143:
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4138:
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4133:
4131:
4128:
4127:
4125:
4123:General terms
4121:
4117:
4110:
4105:
4103:
4098:
4096:
4091:
4090:
4087:
4081:
4078:
4076:
4073:
4071:
4067:
4064:
4061:
4059:
4056:
4053:
4049:
4046:
4043:
4042:John Hutchins
4039:
4035:
4032:
4029:
4027:
4023:
4020:
4017:
4015:
4012:
4011:
4007:
4001:
3997:
3993:
3989:
3984:
3979:
3976:(1): 96–112.
3975:
3971:
3966:
3962:
3957:
3953:
3951:0-12-362830-X
3947:
3942:
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3814:Abadi, Mark.
3810:
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3517:
3513:
3508:
3503:
3499:
3495:
3490:
3489:10.2196/50814
3485:
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3207:
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3197:
3185:
3184:BusinessKorea
3181:
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2018:
2014:
2013:
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2001:
1998:
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1991:9780226000299
1987:
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1941:
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1765:0-8020-5296-7
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1676:IEEE Spectrum
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1480:original work
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1234:MSN Messenger
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929:
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878:deep learning
873:
865:
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839:
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738:
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735:annotations.
734:
730:
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725:morphological
722:
721:deep learning
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692:
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674:
670:
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665:computational
662:
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636:
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630:
629:David G. Hays
626:
624:
620:
616:
608:
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604:
600:
599:
594:
590:
586:
582:
581:Warren Weaver
578:
574:
573:Warren Weaver
570:
565:
563:
559:
555:
551:
547:
546:cryptanalysis
543:
542:cryptographer
539:
531:
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361:Transcription
359:
357:
354:
353:
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346:
341:
338:
336:
335:Organizations
333:
331:
328:
326:
323:
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321:
320:
317:Institutional
315:
310:
307:
305:
302:
300:
297:
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287:
285:
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279:Glocalization
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269:
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261:
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219:
218:
213:
208:
205:
203:
200:
198:
195:
193:
190:
188:
185:
183:
182:Skopos theory
180:
178:
175:
174:
173:
172:
167:
162:
159:
157:
154:
152:
151:Word-for-word
149:
147:
144:
142:
139:
137:
134:
132:
129:
127:
124:
122:
119:
117:
114:
112:
109:
107:
104:
102:
101:Bhagavad-gita
99:
97:
94:
92:
89:
88:
87:
86:
81:
77:
73:
72:
69:
65:
61:
57:
56:
49:
45:
41:
37:
33:
19:
5349:
5236:Robot ethics
5035:Semantic Web
5014:
4914:Interlingual
4877:
4739:Concordancer
4398:
4135:Bag-of-words
3973:
3969:
3960:
3939:
3924:
3901:. Retrieved
3883:
3846:
3837:
3828:
3819:
3809:
3786:
3773:
3765:
3749:
3729:
3717:. Retrieved
3713:the original
3703:
3687:
3671:
3646:
3642:
3590:
3586:
3576:
3567:
3563:
3553:
3541:. Retrieved
3537:
3479:
3475:
3465:
3435:(1): 42–65.
3432:
3428:
3418:
3375:
3371:
3361:
3316:
3312:
3287:. Retrieved
3260:
3256:
3246:
3220:. Retrieved
3199:
3187:. Retrieved
3183:
3173:
3161:. Retrieved
3146:
3136:17 September
3134:. Retrieved
3125:
3115:
3103:. Retrieved
3099:
3090:
3078:. Retrieved
3074:
3064:
3052:. Retrieved
3047:
3038:
3026:. Retrieved
3021:
3012:
3000:. Retrieved
2996:
2986:
2976:13 September
2974:. Retrieved
2960:
2948:. Retrieved
2944:
2934:
2922:. Retrieved
2917:
2908:
2896:. Retrieved
2882:
2870:. Retrieved
2863:the original
2850:
2838:. Retrieved
2834:the original
2829:
2819:
2807:. Retrieved
2787:
2781:
2769:. Retrieved
2753:
2723:
2711:. Retrieved
2704:the original
2695:
2688:
2680:
2677:Claude Piron
2672:
2627:
2622:
2603:
2584:
2565:
2560:
2552:
2513:. Retrieved
2509:
2499:
2477:
2465:. Retrieved
2461:
2452:
2440:. Retrieved
2436:
2426:
2383:
2379:
2369:
2348:
2339:
2310:
2306:
2296:
2284:. Retrieved
2268:
2261:
2249:. Retrieved
2233:
2226:
2214:. Retrieved
2200:
2181:
2175:
2166:
2154:. Retrieved
2145:
2135:
2116:
2110:
2091:
2085:
2074:
2042:
2036:
2028:
2021:. Retrieved
2010:
2000:
1981:
1975:
1963:. Retrieved
1957:(1): 40–43.
1954:
1950:
1940:
1928:. Retrieved
1914:
1904:
1894:
1880:
1874:
1862:. Retrieved
1848:
1833:the original
1798:
1791:
1781:
1774:
1755:
1749:
1737:. Retrieved
1733:the original
1728:
1718:
1706:. Retrieved
1701:
1692:
1680:. Retrieved
1675:
1666:
1457:
1445:
1441:
1423:
1417:
1403:
1399:
1384:, which the
1374:Claude Piron
1362:
1338:
1327:
1316:
1312:
1290:
1278:
1267:
1258:
1254:
1243:
1240:Online games
1211:
1208:Social media
1174:
1149:
1137:
1121:
1112:
1103:
1100:Applications
1090:
1087:
1080:
1073:
1063:
1059:
1057:
1050:
1047:Named entity
1035:
1022:
1017:
1002:, which the
988:Claude Piron
986:
982:
975:
952:
926:
894:
887:
875:
857:
852:
850:
823:
805:
786:
777:Interlingual
767:
751:dictionaries
748:
718:
693:
688:
681:
678:
659:
650:
646:ALPAC report
642:
627:
612:
596:
566:
535:
500:
487:
486:
325:Associations
271:Localization
227:
215:Technologies
44:
5300:Moore's law
5231:Neuroethics
5226:Cyberethics
5047:Atomtronics
4904:Statistical
4696:Topic model
4576:Text corpus
4422:Statistical
4289:Text mining
4130:AI-complete
3903:24 November
3232:cite report
3155:. Gcn.com.
3022:VentureBeat
2515:18 December
2313:: 179–222.
2156:22 November
2146:Digital.com
1739:2 September
1611:Postediting
1606:Phraselator
1567:("howlers")
1501:AI-complete
1395:meaningless
1386:grammatical
1382:source text
1378:ambiguities
1230:Google Talk
1004:grammatical
1000:source text
996:ambiguities
899:generative
814:Statistical
790:interlingua
587:machine at
569:A. D. Booth
554:probability
507:statistical
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