by Michel Lopez
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 necessarily perfect.
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 over).
- 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 as possible.
- Don’t over-correct 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!