Advice to freelance translators on MT post-editing projects
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MT post-editing projects can be divided into two main
categories, depending on the expected level of quality of the final output:
- Perfection: The objective is to get final files
indistinguishable from files that would have been handled only by humans
through a standard translation process.
- Readability: The objective is only to get final
files that have the same meaning as the source files, are correct from
grammar, spelling, and terminology standpoints, but whose style is not
For marketing content, “perfection” is clearly a must, but for technical
manuals, “readability” can be deemed sufficient.
Thanks to the large number of projects we have been handling at e2f
translations, the largest English to French single language vendor, we
have been able to categorize them as “Good” or “Bad” from a production
perspective. Unfortunately, we often had to wait until the post-mortem
phase to know whether the project was “Good” or “Bad!”
The following are some of the characteristics of a “Good” project:
- Source files have been written or edited for machine translation:
either the source text was written in very simple and consistent language,
with short sentences, straightforward word order and little redundancy,
or the files have been processed through a “content cleaning software”
such as Acrolinx in order to achieve the same results.
- The glossary is comprehensive and well translated, and the engine
uses it in a systematic manner.
- The project is large, it has been divided into batches and each batch
is processed individually, after incorporation into the MT engine of
final output from the previous batch.
- Specific linguist feedback is incorporated into the engine (fine-tuning
of grammar rules, updates to the glossary, etc.), and the linguist is
financially rewarded for this step.
When all of the above is true, the linguist feels involved and the quality
of the output increases throughout the project, along with the productivity
and happiness of the linguist!
In “Bad” projects, the opposite happens:
- Source files are poorly written, terminology is inconsistent, sentences
are long, grammar is awkward, etc.
- The glossary is too small or inadequate and/or it’s not being used
consistently by the engine.
- Even though the project is large, the machine translation engine
has been run only once at the onset.
In this type of project, the linguist gets increasingly frustrated, as
the same mistakes have to be corrected over and over again, while the
overall productivity remains unchanged.
In order to increase productivity while editing MT output, we have found
that it is best to abide by the following rules:
- Read the sentence in the target language first:
- If the sentence is very long, erase it and translate from scratch
(the longer the sentence, the more likely it is that the engine will
have made a large number of mistakes and that it will be faster to start
- If the sentence is short but does not make sense, erase it and translate
from scratch (if you are going to change most of the words, you might
as well start over).
- Otherwise, read the source text and edit the target text, as little
- Don’t overcorrect for styles and synonyms.
To summarize, the best advice we can give to freelance translators willing
to take the plunge into MT post-editing is:
- Clarify expectations at the project onset (so you don’t end up getting
paid for “Readable” quality while providing “Perfect” quality).
- Look for “Good” projects and stay away from “Bad” projects, unless
you would rather feel frustrated than involved!
- Use best post-editing practices to increase your productivity.
- Finally, calculate your productivity and adapt your rate accordingly!
Very similar advice can be applied to standard translation projects,
which proves that MT engines are just another tool and not the revolution
some linguists are scared about!
Published - March 2013
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