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Postediting (or post-editing) “is the process of improving a machine-generated translation with a minimum of manual labour”.[1] A person who postedits is called a posteditor. The concept of postediting is linked to that of pre-editing. In the process of translating a text via machine translation, best results may be gained by pre-editing the source text – for example by applying the principles of controlled language – and then postediting the machine output. It is distinct from editing, which refers to the process of improving human generated text (a process which is often known as revision in the field of translation). Postedited text may afterwards be revised to ensure the quality of the language choices or proofread to correct simple mistakes.

Postediting involves the correction of machine translation output to ensure that it meets a level of quality negotiated in advance between the client and the posteditor. Light postediting aims at making the output simply understandable; full post-editing at making it also stylistically appropriate. With advances in machine translation full postediting is becoming an alternative to manual translation. There are a number of software tools that support post-editing of machine translated output. This includes the Google Translator Toolkit, SDL Trados, Unbabel and Systran.[2] [3]

Postediting and machine translation[edit]

Machine translation left the labs to start being used for its actual purpose in the late seventies at some big institutions such as the European Commission and the Pan-American Health Organization, and then, later, at some corporations such as Caterpillar and General Motors. First studies on postediting appeared in the eighties, linked to those implementations.[4] To develop appropriate guidelines and training, members of the Association for Machine Translation in the Americas (AMTA) and the European Association for Machine Translation (EAMT) set a Post-editing Special Interest Group in 1999.[5]

After the nineties, advances in computer power and connectivity sped machine translation development and allowed for its deployment through the web browser, including as a free, useful adjunct to the main search engines (Google Translate, Bing Translator, Yahoo! Babel Fish). A wider acceptance of less than perfect machine translation was accompanied also by a wider acceptance of postediting. With the demand for localisation of goods and services growing at a pace that could not be met by human translation, not even assisted by translation memory and other translation management technologies, industry bodies such as the Translation Automation Users Society (TAUS) expect machine translation and postediting to play a much bigger role within the next few years.[6]

Light and full postediting[edit]

Studies in the eighties distinguished between degrees of postediting which, in the context of the European Commission Translation Service, were first defined as conventional and rapid[7] or full and rapid.[8] Light and full postediting seems the wording most used today.

Light postediting implies minimal intervention by the posteditor, as strictly required to help the end user make some sense of the text; the expectation is that the client will use it for inbound purposes only, often when the text is needed urgently, or has a short time span.

Full postediting involves a greater level of intervention to achieve a degree of quality to be negotiated between client and posteditor; the expectation is that the outcome will be a text that is not only understandable but presented in some stylistically appropriate way, so it can be used for assimilation and even for dissemination, for inbound and for outbound purposes.

At the top end of full postediting there is the expectation of a level of quality which is indistinguishable from that of human translation. The assumption, however, has been that it takes less effort for translators to work directly from the source text than to postedit the machine generated version. With advances in machine translation, this may be changing. For some language pairs and for some tasks, and with engines that have been trained with domain specific good quality data, some clients are already requesting translators to postedit instead of translating from scratch, in the belief that they will attain similar quality at a lower cost.

The light/full classification, developed in the nineties when machine translation still came on a CD-ROM, may not suit advances in machine translation at the light postediting end either. For some language pairs and some tasks, particularly if the source has been pre-edited, raw machine output may be good enough for gisting purposes without requiring subsequent human intervention.

Postediting efficiency[edit]

Postediting is used when raw machine translation is not good enough and human translation not required. Industry advises postediting to be used when it can at least double the productivity of manual translation, even fourfold it in the case of light postediting.[citation needed]

However, postediting efficiency is difficult to predict. Various studies from both academia and industry have shown that postediting is generally faster than translating from scratch, regardless of language pairs or translators' experience.[9] There is, however, no agreement about how much time can be saved through postediting in practice: While the industry reports on time savings around 40%,[10] some academic studies suggest that time savings under realistic working conditions are more likely to be between 15–20%.[11]

Postediting and the language industry[edit]

After some thirty years, postediting is still “a nascent profession”.[12] What the right profile of the posteditor is has not yet been fully studied. Postediting overlaps with translating and editing, but only partially. Most think the ideal posteditor will be a translator keen to be trained on the specific skills required, but there are some who think a bilingual without a background in translation may be easier to train.[13] Not much is known either on who the actual posteditors are, whether they work mostly as in-house employees or freelancers, and on which conditions.

There are not clear figures on how big the postediting pie is within the translation industry. A recent survey showed 50% of language service providers offered it, but for 85% of them it accounted less than 10% of their throughput.[14] Unbabel, a crowdsourcing postediting translation service, has translated more than 11.000.000 words (November 2014)[15] [16]

Productivity and volume estimates are, in any case, moving targets since advances in machine translation, in a significant part driven by the postedited text being fed back into its engines, will mean the more postediting is done, the higher the quality of machine translation and the more widespread postediting will become.[citation needed]

See also[edit]


  1. ^ "Postediting in Practice. A TAUS Report, March 2010 p.6.". Archived from the original on 2009-10-31. 
  2. ^ "GTS Blog".  External link in |title= (help);
  3. ^ "Unbabel - How it works".  External link in |title= (help);
  4. ^ "Senez, Dorothy. "Post-editing service for machine translation users at the European Commission". Translating and the Computer 20. Proceedings from Aslib conference, 12 & 13 November 1998; Vasconcellos, M. and M. Léon (1985). "SPANAM and ENGSPA: Machine Translation at the Pan American Health Organization". Computational Linguistics 11, 122-136.". 
  5. ^ "Allen, Jeffrey. "Post-editing", in Harold Somers (ed.) (2003). Computers and Translation. A translator’s guide. Benjamins: Amsterdam/Philadelphia, p. 312.". 
  6. ^ "TAUS website".  External link in |title= (help);
  7. ^ "Loffler-Laurian, Anne marie. (1986). Post-édition rapide et post-édition conventionelle: deux modalities d’una activité spécifique¨. Multilingua 5, 81-88.". 
  8. ^ "Wagner, Elisabeth. "Rapid post-editing of Systran" Translating and the Computer 5. Proceedings from Aslib conference, 10 & 11 November 1983.". 
  9. ^ "Green, Spence, Jeffrey Heer, and Christopher D. Manning (2013): The Efficacy of Human Post-Editing for Language Translation, ACM Human Factors in Computing Systems (CHI).".  External link in |title= (help);
  10. ^ "Plitt, Mirko and Francois Masselot (2010): A Productivity Test of Statistical Machine Translation Post-Editing in A Typical Localisation Context, Prague Bulletin of Mathematical Linguistics, 93:7–16.".  External link in |title= (help);
  11. ^ "Läubli, Samuel, Mark Fishel, Gary Massey, Maureen Ehrensberger-Dow, and Martin Volk (2013): Assessing post-editing efficiency in a realistic translation environment, Proceedings of the 2nd Workshop on Post-editing Technology and Practice (WPTP), p. 83–91.".  External link in |title= (help);
  12. ^ "TAUS website".  External link in |title= (help);
  13. ^ "Hutchins, John (1995). Reflections on the History and present state of machine translation. Retrieved from". 
  14. ^ "Postediting in Practice. A TAUS Report. March 2010, p. 13.".  External link in |title= (help);
  15. ^ "Unbabel Launches A Human-Edited Machine Translation Service To Help Businesses Go Global, Localize Customer Support".  line feed character in |title= at position 116 (help); External link in |title= (help);
  16. ^ "Unbabel".  External link in |title= (help);

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