Editing Machine translation (section)

==Applications==
While no system provides the ideal of fully automatic high-quality machine translation of unrestricted text, many fully automated systems produce reasonable output.<ref>{{Cite book |url=http://www.benjamins.com/cgi-bin/t_bookview.cgi?bookid=BTL%2014 |title=Melby, Alan. The Possibility of Language (Amsterdam:Benjamins, 1995, 27–41) |publisher=Benjamins.com |year=1995 |isbn=9789027216144 |access-date=2012-06-12 |archive-url=https://web.archive.org/web/20110525234319/http://www.benjamins.com/cgi-bin/t_bookview.cgi?bookid=BTL%2014 |archive-date=25 May 2011 |url-status=live}}</ref><ref>{{Cite web |last=Wooten |first=Adam |date=14 February 2006 |title=A Simple Model Outlining Translation Technology |url=http://tandibusiness.blogspot.com/2006/02/simple-model-outlining-translation.html |archive-url=https://archive.today/20120716095630/http://tandibusiness.blogspot.de/2006/02/simple-model-outlining-translation.html |archive-date=16 July 2012 |access-date=2012-06-12 |website=T&I Business}}</ref><ref>{{cite web |url=http://www.mt-archive.info/Bar-Hillel-1960-App3.pdf |title=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. |access-date=2012-06-12 |archive-date=28 September 2018 |archive-url=https://web.archive.org/web/20180928203641/http://www.mt-archive.info/Bar-Hillel-1960-App3.pdf |url-status=dead }}</ref> The quality of machine translation is substantially improved if the domain is restricted and controlled.<ref>{{cite web |url=http://tauyou.com/blog/?p=47 |title=Human quality machine translation solution by Ta with you |language=es |publisher=Tauyou.com |date=15 April 2009 |access-date=2012-06-12 |archive-date=22 September 2009 |archive-url=https://web.archive.org/web/20090922094140/http://tauyou.com/blog/?p=47 |url-status=live }}</ref> 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.

===Travel===
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 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.<ref>{{cite news |title=Google Translate Adds 20 Languages To Augmented Reality App |url=https://www.popsci.com/google-translate-adds-augmented-reality-translation-app/ |access-date=9 January 2023 |work=Popular Science |date=30 July 2015}}</ref> It can also [[speech recognition|recognize speech]] and then translate it.<ref>{{cite news |last1=Whitney |first1=Lance |title=Google Translate app update said to make speech-to-text even easier |url=https://www.cnet.com/tech/services-and-software/google-translation-app-may-better-recognize-certain-languages/ |access-date=9 January 2023 |work=CNET |language=en}}</ref>

===Public administration===
Despite their inherent limitations, MT programs are used around the world. Probably 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).<ref>{{cite web|url=http://ec.europa.eu/isa/actions/02-interoperability-architecture/2-8action_en.htm|title=Machine Translation Service|date=5 August 2011|access-date=13 September 2013|archive-date=8 September 2013|archive-url=https://web.archive.org/web/20130908232212/http://ec.europa.eu/isa/actions/02-interoperability-architecture/2-8action_en.htm|url-status=live}}</ref>

===Wikipedia===
Machine translation has also been used for translating [[Wikipedia]] 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.<ref>{{cite news |last1=Wilson |first1=Kyle |title=Wikipedia has a Google Translate problem |url=https://www.theverge.com/2019/5/8/18526739/wikipedia-translation-tool-machine-learning-ai-english |access-date=9 January 2023 |work=The Verge |date=8 May 2019}}</ref><ref>{{cite news |title=Wikipedia taps Google to help editors translate articles |url=https://venturebeat.com/ai/wikipedia-taps-google-to-help-editors-translate-articles/ |access-date=9 January 2023 |work=VentureBeat |date=9 January 2019}}</ref><ref>{{cite web |title=Content translation tool helps create over half a million Wikipedia articles |url=https://wikimediafoundation.org/news/2019/09/23/content-translation-tool-helps-create-over-half-a-million-wikipedia-articles/ |website=Wikimedia Foundation |access-date=10 January 2023 |date=23 September 2019}}</ref> English-language articles are thought to usually be more comprehensive and less biased than their non-translated equivalents in other languages.<ref>{{cite web |last1=Magazine |first1=Undark |title=Wikipedia Has a Language Problem. Here's How To Fix It. |url=https://undark.org/2021/08/12/wikipedia-has-a-language-problem-heres-how-to-fix-it/ |website=Undark Magazine |access-date=9 January 2023 |date=12 August 2021}}</ref> As of 2022, [[English Wikipedia]] has over 6.5 million articles while, for example, the [[German Wikipedia|German]] and [[Swedish Wikipedia]]s each only have over 2.5 million articles,<ref>{{cite web |title=List of Wikipedias - Meta |url=https://meta.wikimedia.org/wiki/List_of_Wikipedias |website=meta.wikimedia.org |access-date=9 January 2023 |language=en}}</ref> each often far less comprehensive.

===Surveillance and military===
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 language|Pashto]] and [[Dari (Eastern Persian)|Dari]] languages.{{Citation needed|date=February 2007}} Within these languages, the focus is on key phrases and quick communication between military members and civilians through the use of mobile phone apps.<ref>{{cite journal |last=Gallafent |first=Alex |title=Machine Translation for the Military |journal=PRI's the World |date=26 Apr 2011 |access-date=17 Sep 2013 |url=http://www.theworld.org/2011/04/machine-translation-military/ |archive-date=9 May 2013 |archive-url=https://web.archive.org/web/20130509171415/http://www.theworld.org/2011/04/machine-translation-military/ |url-status=live }}</ref> The Information Processing Technology Office in [[DARPA]] hosted programs like [[DARPA TIDES program|TIDES]] and [[Babylon translator]]. US Air Force has awarded a $1 million contract to develop a language translation technology.<ref>{{cite web |last=Jackson |first=William |url=http://gcn.com/articles/2003/09/09/air-force-wants-to-build-a-universal-translator.aspx |title=GCN – Air force wants to build a universal translator |publisher=Gcn.com |date=9 September 2003 |access-date=2012-06-12 |archive-date=16 June 2011 |archive-url=https://web.archive.org/web/20110616052943/http://gcn.com/articles/2003/09/09/air-force-wants-to-build-a-universal-translator.aspx |url-status=live }}</ref>

===Social media===
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.

==== Online games ====
[[Lineage W]] gained popularity in Japan because of its machine translation features allowing players from different countries to communicate.<ref>{{Cite web |last=Young-sil |first=Yoon |date=2023-06-26 |title=Korean Games Growing in Popularity in Tough Japanese Game Market |url=http://www.businesskorea.co.kr/news/articleView.html?idxno=117105 |access-date=2023-08-08 |website=BusinessKorea |language=}}</ref>

===Medicine===
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,<ref>{{Cite report |url=http://www.nap.edu/html/alpac_lm/ARC000005.pdf |title=Language and Machines: Computers in Translation and Linguistics |last=Automatic Language Processing Advisory Committee, Division of Behavioral Sciences, National Academy of Sciences, National Research Council |date=1966 |publisher=National Research Council, National Academy of Sciences |location=Washington, D. C. |access-date=21 October 2013 |archive-url=https://web.archive.org/web/20131021044934/http://www.nap.edu/html/alpac_lm/ARC000005.pdf |archive-date=21 October 2013 |url-status=live}}</ref> 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 the importance of accurate translations in medical diagnoses.<ref>{{cite journal|url=http://www.cfp.ca/content/59/4/382.full|title=Using machine translation in clinical practice|journal=Canadian Family Physician|date=April 2013|volume=59|issue=4|pages=382–383|access-date=21 October 2013|archive-date=4 May 2013|archive-url=https://web.archive.org/web/20130504040732/http://www.cfp.ca/content/59/4/382.full|url-status=live|last1=Randhawa|first1=Gurdeeshpal|last2=Ferreyra|first2=Mariella|last3=Ahmed|first3=Rukhsana|last4=Ezzat|first4=Omar|last5=Pottie|first5=Kevin|pmid=23585608|pmc=3625087}}</ref>

Researchers caution that the use of machine translation in medicine could risk mistranslations that can be dangerous in critical situations.<ref name=":02">{{Cite journal |last1=Vieira |first1=Lucas Nunes |last2=O’Hagan |first2=Minako |last3=O’Sullivan |first3=Carol |date=2021-08-18 |title=Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases |journal=Information, Communication & Society |language=en |volume=24 |issue=11 |pages=1515–1532 |doi=10.1080/1369118X.2020.1776370 |s2cid=225694304 |issn=1369-118X|doi-access=free |hdl=1983/29727bd1-a1ae-4600-9e8e-018f11ec75fb |hdl-access=free }}</ref><ref>{{Cite journal |last1=Khoong |first1=Elaine C. |last2=Steinbrook |first2=Eric |last3=Brown |first3=Cortlyn |last4=Fernandez |first4=Alicia |date=2019-04-01 |title=Assessing the Use of Google Translate for Spanish and Chinese Translations of Emergency Department Discharge Instructions |journal=JAMA Internal Medicine |language=en |volume=179 |issue=4 |pages=580–582 |doi=10.1001/jamainternmed.2018.7653 |issn=2168-6106 |pmc=6450297 |pmid=30801626}}</ref> 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.<ref>{{Cite journal |last=Piccoli |first=Vanessa |date=2022-07-05 |title=Plurilingualism, multimodality and machine translation in medical consultations: A case study |url=http://www.jbe-platform.com/content/journals/10.1075/tis.21012.pic |journal=Translation and Interpreting Studies |language=en |volume=17 |issue=1 |pages=42–65 |doi=10.1075/tis.21012.pic |s2cid=246780731 |issn=1932-2798|url-access=subscription }}</ref><ref>{{Cite journal |last1=Herrera-Espejel |first1=Paula Sofia |last2=Rach |first2=Stefan |date=2023-11-20 |title=The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review |journal=JMIR Public Health and Surveillance |language=en |volume=9 |pages=e50814 |doi=10.2196/50814 |pmid=37983078 |issn=2369-2960 |doi-access=free |pmc=10696499 }}</ref>

=== Law ===
[[Legal English|Legal language]] 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.<ref name=":1">{{Cite web |last=legalj |date=2023-01-02 |title=Man v. Machine: Social and Legal Implications of Machine Translation |url=https://legaljournal.princeton.edu/man-v-machine-social-and-legal-implications-of-machine-translation/ |access-date=2023-12-04 |website=Princeton Legal Journal |language=en-US}}</ref> 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 [[Legal proceeding|formal proceedings]].<ref>{{Cite journal |last=Chavez |first=Edward L. |date=2008 |title=New Mexico's Success with Non-English Speaking Jurors |url=https://heinonline.org/HOL/Page?handle=hein.journals/jrlci1&id=307&div=&collection= |journal=Journal of Court Innovation |volume=1 |pages=303}}</ref>

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 the providers of the translation tools.<ref name=":1" /> 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.<ref name=":02"/>

=== Ancient languages ===
The advancements in [[convolutional neural network]]s 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 [[Akkadian language|Akkadian]] and its dialects Babylonian and Assyrian.<ref>{{Cite journal |last1=Gutherz |first1=Gai |last2=Gordin |first2=Shai |last3=Sáenz |first3=Luis |last4=Levy |first4=Omer |last5=Berant |first5=Jonathan |date=2023-05-02 |editor-last=Kearns |editor-first=Michael |title=Translating Akkadian to English with neural machine translation |url=https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgad096/7147349 |journal=PNAS Nexus |language=en |volume=2 |issue=5 |pages=pgad096 |doi=10.1093/pnasnexus/pgad096 |issn=2752-6542 |pmc=10153418 |pmid=37143863}}</ref>