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On the usefulness of machine translation

Anyone who knows me knows that I’m not a fan of offering machine translation post-editing. There is just so much to fix when you work this way that it’s not worth it, especially if you’re a perfectionist like me, who would find it to difficult to leave a sentence alone that can be understood but leaves much room for improvement.

I don’t, however, belong to the camp who think that machine translation (MT) is never useful. In fact, I challenge anyone to tell me it would not save them time in the following example.

The sentence contains a list of nine adjectives referring to fields of study, and although the English equivalents are quite similar, editing each word would be quite fiddly. This is the kind of thing MT is very good at getting right. Here’s what DeepL offered:

There are a couple of problems with this sentence, but in the middle of it we have the list of adjectives translated perfectly. I double checked that my preferred spelling was used for “palaeontological” and “archaeological”.

At the start of the sentence I then added the article “The”, since I know the surrounding context (which MT ignores): it’s talking about specific research being carried out, rather than research in general.

At the end of the sentence, I removed the “etc.”, which doesn’t sound good between an adjective and a noun. In its stead, I added “such as” before the list of adjectives, to make it clear that other fields of study might be included.

(After writing this post, I considered the possibility of replacing the adjectives with nouns. For example, “The research should focus on fields such as biology, hydrology, geology…”. If I had decided to do that then it probably would have been quicker to dictate from scratch.)

“Fair enough. But for most sentences, MT is useless!”

The danger with MT is that we might use it for sentences where we’d get a much better translation working from scratch. Right below the previous example, my text contained a list of strategies for the research that would be carried out. The first strategy read as follows:

For this sentence, a quick glance at the results page showed that Google’s solution (number 1) would probably be more useful than DeepL’s (number 2), since it used the list format I was using and started with the verb proper and a capital letter, rather than the word “to”:

Nevertheless, the proposal was still not particularly helpful:

I therefore decided to move my cursor past the “a)” and dictate, resulting in this:

After dictating my translation, my cursor was positioned after the full stop between the end of my translation and the start of the machine translation. I then used the voice command “delete and validate”, which I have programmed to send the following keystrokes to my computer-assisted translation tool, MemoQ:

  • shift+ctrl+pgdown (select to the end of the segment)
  • delete (delete the selected text)
  • ctrl+enter (validate the segment in MemoQ).

Although I did not use the MT solution for this sentence, its appearance in the MemoQ translation grid did not cost me any time, as I still needed to use only one voice command to validate my translation after completing it.

I should add that, if I were not dictating (for example, while working on a train), I would probably have done things slightly differently, taking advantage of the words “a list of priority”, which already appeared in the MT proposal. I’d have proceeded as follows:

  • Move the cursor past “a)”
  • Type “Draw up”
  • Hit ctrl+delete to delete “Establish”
  • Move the cursor past “a list of priority” by pressing ctrl+right four times
  • Type the rest of my translation
  • Delete the rest of the MT segment (either ctrl+pg+down then delete or press and hold ctrl+delete)
  • Validate the segment (ctrl+enter).

(The last two steps could be consolidated into a single hotkey with a tool like AutoHotkey.)


I agree with everything my colleagues say about machine translation post-editing, which is not a service I will be offering. However, the above example shows that we can leverage MT to raise our productivity without compromising the quality of the translation.

Because my approach uses MT only where it writes what I want to write, there is no loss of quality, but there are productivity gains with some sentences and segments.


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