Editing Machine translation (section)

===Disambiguation===
{{Main|Word-sense disambiguation|Syntactic disambiguation}}
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]].<ref>[http://ourworld.compuserve.com/homepages/WJHutchins/Miles-6.htm Milestones in machine translation – No.6: Bar-Hillel and the nonfeasibility of FAHQT] {{webarchive|url=https://web.archive.org/web/20070312062051/http://ourworld.compuserve.com/homepages/WJHutchins/Miles-6.htm |date=12 March 2007 }} by John Hutchins</ref> He pointed out that without a "universal encyclopedia", a machine would never be able to distinguish between the two meanings of a word.<ref>Bar-Hillel (1960), "Automatic Translation of Languages". Available online at http://www.mt-archive.info/Bar-Hillel-1960.pdf {{Webarchive|url=https://web.archive.org/web/20110928112348/http://www.mt-archive.info/Bar-Hillel-1960.pdf |date=28 September 2011 }}</ref> 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 a comprehensive knowledge of the word. So far, shallow approaches have been more successful.<ref>{{Cite book|title=Hybrid approaches to machine translation|others=Costa-jussà, Marta R., Rapp, Reinhard, Lambert, Patrik, Eberle, Kurt, Banchs, Rafael E., Babych, Bogdan|date=21 July 2016|isbn=9783319213101|location=Switzerland|oclc=953581497}}</ref>

[[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 [[ambiguity|ambiguities]] in the [[source text]], which the [[grammatical]] and [[Lexical (semiotics)|lexical]] exigencies of the [[Translation|target language]] require to be resolved:

{{Blockquote|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, 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.<ref name="piron">[[Claude Piron]], ''Le défi des langues'' (The Language Challenge), Paris, L'Harmattan, 1994. <!-- GFDL translation by Jim Henry --></ref>
}}

The ideal deep approach would require the translation software to do all the research necessary for this kind of disambiguation on its own; but this would require a higher degree of [[AI]] than has yet been attained.  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 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.