Translators Under Pressure

A black and white photo of cogwheels
Photo by Pixabay on Pexels.com

In the midst of the AI-powered machine translation hype, the translation industry finds itself grappling with a shortage of talent

This article delves into the insights from the 2023 Translation Technology Insights survey conducted by RWS.

The Growing Skills Shortage:

The statistics are alarming. Not only do most LSPs face the challenge of talent shortage, but there’s also a darker trend emerging.

Graph showing translator experience in the industry, comparing 2020 and 2023
Source: RWS Translation Technology Insights 2023

Graph illustrating the decline in experienced translators from 2020 to 2023:
According to the RWS Translation Technology Insights 2023 report, there has been a 9% reduction in the number of highly experienced linguists in the market over the span of mere 3 years. Concurrently, there is a rise in the percentage of new entrants.

This trend aligns with the findings of the ProZ.com 2022 report, where 37% of translators stated that they knew someone who had left the industry.

However, I am skeptical of RWS’s interpretation that this outflow is primarily due to retirement. Firstly, the scale of the exodus is significant, and secondly, translation has always been viewed as a profession that one can continue even after retirement or in poor health. While some activities can be reduced, why would someone leave the entire business?

So, what is really happening? The answer lies in the immense pressure experienced by translation professionals.

Key Pressure Points for Freelancers:

  • 42% face pressure for cheaper translations.
  • 29% face pressure for faster delivery.
  • Only 18% cite higher quality as a primary concern.

The reasons mentioned above are self-explanatory. If experienced professionals are leaving the industry at an increasing rate, retirement cannot be the main driver.

The Harsh Reality:

As translation professionals struggle under the constant pressure to adopt new technologies and deliver faster, the demand for lower prices has reached a threshold that many cannot sustain.

Newcomers to the industry are often willing to work for reduced rates, hoping to increase their rates once they gain sufficient knowledge and references. However, as this does not materialize, after around five years or more, when they have families to support, they start leaving.

Experienced language professionals face mounting pressure to sacrifice their time management, continuously learn new tools under stressful conditions, and endure disrespectful requests to lower their rates. In such circumstances, leaving the industry becomes an appealing option.

The Impact of New Technologies:

While translators hear promising stories about new technologies in the industry, let’s be clear: for the most part, it is the language service providers (LSPs) who benefit.

Although these technologies may enhance effectiveness (which is not always the case), LSPs have adjusted their metrics to demand more words translated within shorter timeframes and at lower fees. The paradox lies in the fact that the resulting fees for translators are lower than before the adoption of these technologies, while the work becomes more exhausting.

The Future Outlook:

The optimistic view based on the RWS survey is that the skills shortage will further drive the adoption of new technologies to “free human translators for the kind of work that machine translators aren’t suited for.”

However, the reality is that human translators’ capacity is being utilized not for highly specific and creative work, but for endless reviewing of an increasing volume of machine-generated words. This shift from creative and inventive work to mechanical tasks is draining, unattractive, and demoralizing.

A staggering 21% of language professionals who do not possess computer-aided translation tools cite they either find them too expensive or not worth the investment. This clearly indicates that they are underpaid to the extent that they can’t afford essential work tools. Additionally, they do not expect to benefit from increased productivity. In other words, they recognize that these tools would enable them to secure jobs from other sources, but their profits would not grow.

LSPs and clients will continue to push reflecting the “easier” character of work in their collaboration terms, resulting in declining incomes for translators. Alongside the mounting pressure to process a growing number of files and smaller jobs, the attractiveness of the profession is diminishing.

As unfavorable payment terms extend to highly skilled and experienced professionals, too, the absolute income numbers are declining rather than growing even for highly skilled, this makes the prospects for newcomers even more dismal.

Moreover, the shortage of skilled individuals will intensify the pressure to further rely on machines, and where senior expertise is lacking, LSPs will attempt to replace it with more sophisticated QA tools. With a major part of clients lacking the knowledge allowing them to estimate the benefits of having a professional linguist, I believe we’ll also see a decline in work opportunities for senior reviewers who, though critically missing, might feel forced out of the industry.

In conclusion, the translation industry finds itself in a critical situation. I dare say this is a point of no return. While machine translation technologies continue to advance, it is crucial to address the talent shortage and alleviate the growing pressure on translators to ensure a sustainable and prosperous future for the profession.

In an industry growing constantly in revenue, I believe there must be a way for language professionals to get their fair share. Translators who often negotiate their rates are in general more satisfied with their income. What does that say?

Humans don’t need to apply?

Lokalise introduced their beta AI implementation. I expected CATs to do better with AI. Why was I not impressed?

While I can imagine playing nerdishly with any AI feature, I can’t imagine a language professional fussing about it in their everyday efforts. Honestly, I believe that ChatGPT technology is amazing and that it’s going to be a game changer in every language-oriented industry. What I missed, was any sense of purpose of its Lokalise implementation. Or maybe I have sensed the purpose and I was not at all happy about what I saw. It seems that the Lokalise AI implementation is NOT intended for professional linguists.


Dear CAT producers: Each time you say “just a single click of a mouse,” a kitten dies.

Or my hand ends in pain. Mouse is NOT the tool of choice for language professionals. It’s keyboard shortcuts. Professional work environment is not mouse based, as it cripples translator’s hands and forces translators to constantly leave the keyboard, move hands there and back, in result delaying their work.

Now back to Lokalise and the AI feature: You’re working in Lokalise. You open a string and you can immediately translate, or use a translation memory match, or one of three machine translation suggestions. As a professional translator, you’ll have the translation finalized in seconds if the string is short, maybe in one or two minutes.

Now with the new option, you can also click the AI feature (it does not offer it’s suggestion immediately, it probably can’t be that fast). You wait a moment for AI to generate it’s suggestion. Then you can also ask it to generate variations. So you click Variations and wait another moment to enjoy a set of new suggestions.

AI is, unlike MT, context aware. It can even work with your glossary (which is great, no irony). To add context, you click another option, write the context, click to save and have AI generate a new sentence. Click. You can ask AI to make it shorter. Click. You can set a character limit. Click, click. You can ask it to SEO optimize. Click. (No, the keywords suggested and implemented by ChatGPT will NOT be as good as those selected by a SEO optimization expert, tested in person.)

The only thing you can’t do is understand why spend five or ten minutes clicking and fussing about a single string? Well, some clicking will be avoidable, as Lokalise is preparing a method for providing AI with context in batches, so you don’t have to define that you want a button text each time. They are also working on AI batch pretranslation. OK. But otherwise?

Maybe sometimes when your memory tricks you and the good wording for a specific sentence is not coming, you can ask AI to step in. But what about the hype about replacing translators?

But here’s the key: “As a project manager, how can you feel confident about approving a translation suggested by AI if you don’t speak the language?”

That question was indeed asked during the webinar. It felt like CAT producers are trying to sell the tool to unsuspecting customers, pretending to sell a tool producing ready-made translation products. Not computer-aided translation anymore. Just computer translation.

‘No, actually you can’t.’ Well, this honest answer never came.

The actual reply was: “…but with human translations it’s the same. If you don’t speak the language, it’s pretty hard to validate the quality of it.” Miguel Caetano, Lokalise Product Marketing Lead said, and continued about a QA tool Lokalise is yet to create, “so you’ll be very quickly able to understand the quality of the translations.”


Fun fact: The currently non-existent QA tool is to be based on the same AI. So the AI will generate some translations, then check them and return the user some metrics on how well it thinks it was doing, right?

And, finally, the speaker added: “… It’s always good to have a human reviewing the content,” mentioning that it could be anyone from the client’s team speaking the language.

Buy the “ultimate do-it-all” tool, it will perform the task alone and then check its own quality, so that you it can show you a nice report saying that it did a really nice job. You can, of course, use an expensive plugin called the “human”. Doesn’t have to be an expert, “human” is enough of a qualification. Forget about technical experts, proofreaders, copywriters. For my next job, I’ll ask my kids or my neighbour for their input. Go HUMANS!

How are you doing, Hal?

P.S.: The accuracy rate achieved by ChatGPT was 85%+ but it did much better in some specialized tests, e.g. in medicine. Its translation success rate varies by language combination.


Translators Raging Against Machines, Part 2

I’ve seen lots of bad translations before but this time the errors were quite consistently eyebrow-raising. I actually couldn’t imagine why anyone would write what I saw. It turned out the text was a poorly edited machine translation.

What’s going on in the translation business?
Deep learning and AI brought a great deal of developments to translation services. Sadly, it seems no one has ever considered that NOT ONLY machines are to learn. Translators (or post-editors) are not prefabricated, ready-to-use androids produced by universities. They keep learning for the whole of their careers. More specifically, above others, they learn from what they read and translate.

Is active contact with texts in foreign languages still a good learning source?
Translators gain knowledge all the time. While reading, watching TV, chatting with others, and while translating. Whatever is new to the translator, has to be researched in dictionaries, expert literature, other sources. This of course requires some effort, so new terms naturally find their spots in the translator’s memory and/or private glossary. However, during the post-editing process everything is already “translated.” And that’s a problem.

I’ve already seen this – therefore I know it.
In the course of machine translation post-processing (MTPE), errors may go undetected when the post-editor is not acquainted with the terminology enough to notice that the machine got it wrong. Moreover, the machine may repeatedly expose the post-editor to the same wrong translation, initiating the human learning process. When you encounter something 5 or more times, you feel acquainted with that, like with an old friend. Your memory tricks you – something looks familiar, so it “must be OK.” Instead of a corrected text, we acquire a human who has just learned a wrong, machine-made translation.

Machines have learned from us already, what about us – humans?
While machines already have the data and keep on learning, every translator once starts from scratch. While my generation of translators developed knowledge on man-made texts and dictionaries compiled meticulously by professional linguists, the generation of post-editors will source their knowledge from machines, too. Including the errors. There is a fair risk that their resulting outputs will be recycled by machines, then by humans, and so on.

An endless, unbreakable cycle of error.



Machine translation post-editing (MTPE) has become a trend-setter in the translation industry. Praised as great helpers pushing human productivity to new heights, translation engines already process huge translation volumes, their outputs to be “merely refined” by humans. The technological developments being huge indeed, linguists are expected to be happy for getting so great help from the machines. Are they really?

Despite the enthusiasm of big translation industry players who expect huge cost savings on human work, translators, now turned to MT post-editors, often seem to object to the new process. Firstly, their experience indicates a lower MT output quality (meaning more human work) than what financial managers forecast in their productivity sheets. This invariably means linguists making less per hour than before, and despite growing experience with the MTPE process, this financial expectation inconsistency between linguists and clients persists.

Conveyor belt operators of the new age
Poor financial motivation is not the only issue, however. MTPE does not merely mean “you do the translation but more effectively.” The whole character of the profession is changed in essence, turning linguists to conveyor line workers of the Brave New World. An essentially creative task of inventing an idiomatic counterpart of the source text gives way to mechanically checking the machine output, endlessly looking for missing or added words, out-of-context translations, and of course (this being the easiest part) translation pieces that make no sense at all.

Exhausted and demotivated
The linguist is expected not to bother about creating any more – usually they are instructed to use as much of the MT as “usable,” as no one is willing to pay for “unnecessary” man-days. MTPE, however, is no less easy, as it requires humans to invest an inhuman amount of attention. Machines do not make human errors. They make inhuman ones. Maybe also this is the reason why endlessly seeking for machine-made errors could be so tiring.

The more machines, the less happiness?
I wonder whether linguistic students of today are already aware that many of them are not preparing for a creative profession, as their task might be to mechanically search texts, trying to spot where machines got it wrong. Every day, 9 am to 5 pm.

How attractive does that sound, actually?