55-503: The Emerald Lakes ( Māori : Ngarotopounamu , lit. ' Pounamu -hued lakes') are a group of small lakes in Tongariro National Park , named for their distinctive colour. The lakes are the result of water filling explosion craters near the summit of Mount Tongariro , with the colour coming from minerals dissolved from the surrounding landscape, particularly calcium carbonate. The lakes are visible to hikers on
110-592: A "competent performance." Franz Josef Och (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 Google Translate translates roughly enough text to fill 1 million books in one day. Before
165-449: A "do-not-translate" list, which has the same end goal – transliteration as opposed to translation. still relies on correct identification of named entities. 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
220-424: 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. Shallow approaches assume no knowledge of the text. They simply apply statistical methods to the words surrounding the ambiguous word. Deep approaches presume
275-597: 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. 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). SYSTRAN , which "pioneered
330-547: A comprehensive knowledge of the word. So far, shallow approaches have been more successful. Claude Piron , a long-time translator for the United Nations and the World Health Organization , 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 ambiguities in the source text , which
385-633: A human, professional translator. Douglas Hofstadter gave an example of a failure of machine translation: the English sentence "In their house, everything comes in pairs. There's his car and her car, his towels and her towels, and his library and hers." might be translated into French as " Dans leur maison, tout vient en paires. Il y a sa voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et les siennes. " That does not make sense because it does not distinguish between "his" car and "hers". Often, first-generation immigrants create something of
440-456: A literal translation in how they speak their parents' native language. This results in a mix of the two languages that is something of a pidgin . Many such mixes have specific names, e.g., Spanglish or Denglisch . For example, American children of German immigrants are heard using "rockingstool" from the German word Schaukelstuhl instead of "rocking chair". Literal translation of idioms
495-494: 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. The idea of machine translation later appeared in the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. The idea of using digital computers for translation of natural languages
550-512: A program for translating in one direction between English and a major European language of your choice" to run on a PC. 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 Lernout & Hauspie 's GlobaLink. Atlantic Magazine wrote in 1998 that "Systran's Babelfish and GlobaLink's Comprende" handled "Don't bank on it" with
605-456: A public demonstration of its Georgetown-IBM experiment system in 1954. MT research programs popped up in Japan and Russia (1955), and the first MT conference was held in London (1956). David G. Hays "wrote about computer-assisted language processing as early as 1957" and "was project leader on computational linguistics at Rand from 1955 to 1968." Researchers continued to join the field as
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#1732859520973660-692: 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 machine translations should be reviewed by human translators for accuracy, and some courts prohibit its use in formal proceedings . The use of machine translation in law has raised concerns about translation errors and client confidentiality . Lawyers who use free translation tools such as Google Translate may accidentally violate client confidentiality by exposing private information to
715-484: A text. This approach is considered promising, but is still more resource-intensive than specialized translation models. Studies using human evaluation (e.g. by professional literary translators or human readers) have systematically identified various issues 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
770-453: A translation that represents the precise meaning of the original text but does not attempt to convey its style, beauty, or poetry. There is, however, a great deal of difference between a literal translation of a poetic work and a prose translation. A literal translation of poetry may be in prose rather than verse but also be error-free. Charles Singleton's 1975 translation of the Divine Comedy
825-493: A vernacular source or into colloquial language. Limitations on translation from casual speech present issues in the use of machine translation in mobile devices. In information extraction , 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. In
880-406: A work written in a language they do not know. For example, Robert Pinsky is reported to have used a literal translation in preparing his translation of Dante 's Inferno (1994), as he does not know Italian. Similarly, Richard Pevear worked from literal translations provided by his wife, Larissa Volokhonsky, in their translations of several Russian novels. Literal translation can also denote
935-549: Is metaphrase (as opposed to paraphrase for an analogous translation). It is to be distinguished from an interpretation (done, for example, by an interpreter ). Literal translation leads to mistranslation of idioms , which can be a serious problem for machine translation . The term "literal translation" often appeared in the titles of 19th-century English translations of the classical Bible and other texts. Word-for-word translations ("cribs", "ponies", or "trots") are sometimes prepared for writers who are translating
990-456: Is a stub . You can help Misplaced Pages by expanding it . This article about a lake is a stub . You can help Misplaced Pages by expanding it . Literal translation Literal translation , direct translation , or word-for-word translation is a translation of a text done by translating each word separately without looking at how the words are used together in a phrase or sentence. In translation theory , another term for literal translation
1045-531: Is a source of translators' jokes. One such joke, often told about machine translation , translates "The spirit is willing, but the flesh is weak" (an allusion to Mark 14:38 ) into Russian and then back into English, getting "The vodka is good, but the meat is rotten". This is not an actual machine-translation error, but rather a joke which dates back to 1956 or 1958. Another joke in the genre transforms "out of sight, out of mind" to "blind idiot" or "invisible idiot". Machine translation Machine translation
1100-521: Is clearly not a phrase that would generally be used in English, even though its meaning might be clear. Literal translations in which individual components within words or compounds are translated to create new lexical items in the target language (a process also known as "loan translation") are called calques , e.g., beer garden from German Biergarten . The literal translation of the Italian sentence, " So che questo non va bene " ("I know that this
1155-424: Is not good"), produces "(I) know that this not (it) goes well", which has English words and Italian grammar . Early machine translations (as of 1962 at least) were notorious for this type of translation, as they simply employed a database of words and their translations. Later attempts utilized common phrases , which resulted in better grammatical structure and the capture of idioms, but with many words left in
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#17328595209731210-457: Is regarded as a prose translation. The term literal translation implies that it is probably full of errors, since the translator has made no effort to (or is unable to) convey correct idioms or shades of meaning, for example, but it can also be a useful way of seeing how words are used to convey meaning in the source language. A literal English translation of the German phrase " Ich habe Hunger " would be "I have hunger" in English, but this
1265-653: Is substantially improved if the domain is restricted and controlled. This enables using machine translation as a tool to speed up and simplify translations, as well as producing flawed but useful low-cost or ad-hoc translations. 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 mobile translation tools enabling mobile business networking between partners speaking different languages, or facilitating both foreign language learning and unaccompanied traveling to foreign countries without
1320-546: 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. Translations by neural MT tools like DeepL Translator , which is thought to usually deliver the best machine translation results as of 2022, typically still need post-editing by a human. Instead of training specialized translation models on parallel datasets, one can also directly prompt generative large language models like GPT to translate
1375-422: Is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical . These methods have since been superseded by neural machine translation and large language models . The origins of machine translation can be traced back to the work of Al-Kindi ,
1430-1037: The Canadian Hansard corpus, the English-French record of the Canadian parliament and EUROPARL , the record of the European Parliament . 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 CANDIDE from IBM . 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. SMT's biggest downfall included it being dependent upon huge amounts of parallel texts, its problems with morphology-rich languages (especially with translating into such languages), and its inability to correct singleton errors. Some work has been done in
1485-566: The German and Swedish Wikipedias each only have over 2.5 million articles, each often far less comprehensive. Following terrorist attacks in Western countries, including 9-11 , the U.S. and its allies have been most interested in developing Arabic machine translation programs, but also in translating Pashto and Dari languages. Within these languages, the focus is on key phrases and quick communication between military members and civilians through
1540-582: The Tongariro Alpine Crossing when they begin their descent from the highest point of that track, at an altitude of 1,886 metres (6,188 ft). The lakes have previously been infested with Juncus bulbosus , invasive to New Zealand, however since 2019 the New Zealand Department of Conservation have sought to control the weed, which has decreased to undetectable levels due to this work. This Manawatū-Whanganui geography article
1595-417: The grammatical and lexical exigencies of the target language require to be resolved: 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 [more] hours of work. There are ambiguities one has to resolve. For instance,
1650-675: 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, however, and after the ALPAC report (1966), which found that the ten-year-long research had failed to fulfill expectations, funding was greatly reduced. According to
1705-562: 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 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
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1760-405: The advent of deep learning methods, statistical methods required a lot of rules accompanied by morphological , syntactic , and semantic annotations. The rule-based machine translation approach was used mostly in the creation of dictionaries and grammar programs. Its biggest downfall was that everything had to be made explicit: orthographical variation and erroneous input must be made part of
1815-435: 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. The ideal deep approach would require
1870-577: 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 BLEU scores for translation will result from the inclusion of methods for named entity translation. 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
1925-400: The 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
1980-407: 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 computational power increased and became less expensive, more interest was shown in statistical models for machine translation . MT became more popular after the advent of computers. SYSTRAN's first implementation system was implemented in 1988 by
2035-446: 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. English-language articles are thought to usually be more comprehensive and less biased than their non-translated equivalents in other languages. As of 2022, English Misplaced Pages has over 6.5 million articles while
2090-524: The importance of accurate translations in medical diagnoses. 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 translation when there is no other alternative, and that translated medical texts should be reviewed by human translators for accuracy. Legal language poses
2145-463: The largest institutional user is the European Commission . 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). Machine translation has also been used for translating Misplaced Pages articles and could play a larger role in creating, updating, expanding, and generally improving articles in
2200-429: 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 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. Use of
2255-460: The need of the intermediation of a human translator. For example, the Google Translate app allows foreigners to quickly translate text in their surrounding via augmented reality using the smartphone camera that overlays the translated text onto the text. It can also recognize speech and then translate it. Despite their inherent limitations, MT programs are used around the world. Probably
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2310-508: The online service of the French Postal Service 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 developed at Kharkov State University (1991). By 1998, "for as little as $ 29.95" one could "buy
2365-413: The original language. For translating synthetic languages , a morphosyntactic analyzer and synthesizer are required. The best systems today use a combination of the above technologies and apply algorithms to correct the "natural" sound of the translation. In the end, though, professional translation firms that employ machine translation use it as a tool to create a rough translation that is then tweaked by
2420-424: The sentence "Smith is the president of Fabrionix" both Smith and Fabrionix 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 rigid designator is what defines these usages for analysis in statistical machine translation. Named entities must first be identified in
2475-418: The source language analyser in order to cope with it, and lexical selection rules must be written for all instances of ambiguity. Transfer-based machine translation was similar to interlingual machine translation 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
2530-422: The source texts, missing high-quality training data and the severity of frequency of several types of problems may not get reduced with techniques used to date, requiring some level of human active participation. 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 Yehoshua Bar-Hillel . He pointed out that without
2585-417: The text; if not, 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. They may be omitted from the output translation, which would also have implications for the text's readability and message. Transliteration includes finding the letters in the target language that most closely correspond to
2640-548: 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 Wireless World ). A similar application, also pioneered at Birkbeck College at the time, was reading and composing Braille texts by computer. The first researcher in the field, Yehoshua Bar-Hillel , began his research at MIT (1951). A Georgetown University MT research team, led by Professor Michael Zarechnak, followed (1951) with
2695-482: 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. 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 shallow approach that involves "ask
2750-430: The translation. 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 target language was then generated out of the interlingua . The only interlingual machine translation system that
2805-858: The use of mobile phone apps. The Information Processing Technology Office in DARPA hosted programs like TIDES and Babylon translator . US Air Force has awarded a $ 1 million contract to develop a language translation technology. The notable rise of social networking on the web in recent years has created yet another niche for the application of machine translation software – in utilities such as Facebook , or instant messaging clients such as Skype , Google Talk , MSN Messenger , etc. – allowing users speaking different languages to communicate with each other. Lineage W gained popularity in Japan because of its machine translation features allowing players from different countries to communicate. Despite being labelled as an unworthy competitor to human translation in 1966 by
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#17328595209732860-519: 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. 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 usages. This causes errors in translation from
2915-498: The utilization of multiparallel corpora , 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. A deep learning -based approach to MT, neural machine translation has made rapid progress in recent years. However, current consensus
2970-503: 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. Machine translation used a method based on dictionary entries, which means that the words were translated as they are by a dictionary. Statistical machine translation tried to generate translations using statistical methods based on bilingual text corpora, such as
3025-511: Was proposed as early as 1947 by England's A. D. Booth and Warren Weaver at Rockefeller Foundation in the same year. "The memorandum written by Warren Weaver 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 APEXC machine at Birkbeck College ( University of London ) of a rudimentary translation of English into French. Several papers on
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