While the latest advances in AI are worrying writers and artists, the translation industry has been affected by this problem for much longer.
In recent months, artificial intelligences have been the stars of tech news, as they have made impressive progress. Advances that also create concern and anger, because they bring to the fore the question of the replacement of humans by robots and AI. The translation sector has been plagued by these questions for several decades.
Technologies evolve, the labor market too
For a long time, machine translation software like Google Translate drew ridicule for being so word-for-word and unable to grasp the context. This changed in the 2010s with the arrival of neural networks, which manage to process whole sentences in one go to create smoother and more nuanced translations based on context.
The best known neural machine translation software – because it is free – is DeepL. self-proclaimed “best translator in the world” on its site, DeepL was created in 2017 by the German company Linguee, known for the site of the same name, a multilingual dictionary that allows you to find the translation of a word or expression by comparing texts in both languages. DeepL therefore relies on the database of this first site to make more accurate translations.
This has partly changed the work of freelance translators, since clients sometimes approach them with automatically translated texts and only offer them to proofread and correct. A faster and cheaper solution for clients, but frustrating for translators, since sometimes entire paragraphs have to be reworked while being paid less. Since translation software is not infallible, its ubiquity gives translators the bitter impression that their clients prefer quantity over quality.
Work around the limits
Having been interested in machine translation tools for a long time, I regularly try to push them to their limits with a common expression of the French language: “I drank the cup”. Even a child knows that, depending on the context, this phrase could mean that I accidentally swallowed water while swimming. However, advanced machine translation software like DeepL is unable to translate it for me non-literally, even when I add the context of a swim. It is indeed an idiom that does not necessarily have an equivalent in another language, so Linguee’s comparative text database is of no help to it. When the context of the swim is specified, it is possible by clicking on drank (bu) to get many alternative translations. Among them, choked (to choke). It therefore requires human intervention to guide DeepL in the right direction. The site then offers me to save in my glossary that to drink is translated by choke but not to capture the entire expression.
“The examples given so far seem innocuous, but in critical contexts where the translation must be perfectly accurate, such as diplomacy, the slightest error can have serious consequences. For literature and audiovisual, humans will always be more competent to transcribe a particular style, rhythm or emotion. »
For some languages, it also happens that the software makes a detour by English, because it is the language which has the most complete database. This sometimes leads to strange translation errors, as teacher-researcher Pascale Elbaz explained last May during the conference “Will neural machine translation replace humans?” » : she gives the example of a text in Chinese on a calligrapher who also likes to carve seals. Sealin English, is seal. Sealalways in English, can also mean seal. The French translation therefore described a calligrapher who likes to carve seals.
The examples given so far seem innocuous, but in critical contexts where the translation must be perfectly accurate, such as diplomacy, the slightest error can have serious consequences. For literature and audiovisual, humans will always be more competent to transcribe a particular style, rhythm or emotion. Human translators therefore remain indispensable for many reasons.
On the positive side, artificial intelligence can also be an ally of these translators. As neural machine translation improves day by day, it allows them to save time and focus on more complex tasks such as documentary research or adapting references and style to the target audience. Although translators may regret that prices are being driven down by artificial intelligence, they are far from replaceable and may even be more efficient thanks to these technologies.