How Neural Machine Translation can improve the work of translators
In the 2020 ATA Annual Conference in Palm Springs, there was a board. The board contained one question and the attendees were encouraged to answer the question: "Do you use machine translation (MT), and how?" Here are some comments and direct quotes:
"To quickly get the gist of a paragraph. As a quick dictionary when I forget the target word in my native language."
"Out of curiosity! The robots are coming whether we like it or not. We should know what MT is good or not good for."
"To get a good laugh."
"To show how it does NOT work and can never replace the high quality professional work I do."
"Integrated in my computer assisted translation tool to assemble segments."
"Never for any serious kind of translation work! For laughs - to get the gist of some Facebook posts and the likes only!"
"To show how algorithms can't make the mental leaps that humans can!"
"Only when requested by the client."
"Then educate the client on MT, computer-assisted translation, and the difference between the two... and on human translation."
It's curious to see that most of the responses are still on the negative side, that is, they come from that angle that machine translation can be used only reactively: the translator reacts to the suggestion of the MT engine. One of the reasons for this approach is that we still think that machine translation is the same as post-editing. Also, when we think about machine translation output, we usually think of the SMT models that work like a Lego that try to fill the empty segment with the missing piece. With neural machine translation, this is not what is happening. NMT first learns the design of the puzzle, then it learns which pieces form that design. The other problem is that we have some misconceptions about Artificial Intelligence and Neural Translation and we think that it competes with our brains' functions. The human brain is much more complicated than any of the machines invented by humans. We only know its biological and physiological functions and have some ideas about the elusive realms. What Artificial Intelligence does is to process a large amount of data and makes predictions exclusively on the basis of that data. So far we have not reached the level where artificial intelligence can use reason, judgment, or create a strategy. If it is true that AI is modeled on the human brain, only a small part of the brain was included in the study.
We also need to take a closer look at what we mean by the translation process and what is post-editing. For example, if the translator receives fragmented suggestions from the MT, then the translator is actually generating a translation by choosing to accept or reject the suggestions that are being presented to them by MT. "This is a high-cognitive load because the translator's thoughts are constantly interrupted by the machine. On the other hand, post-editing implies that the suggestions by the MT are full suggestions that are good enough to read them."
The next question is, and I believe that the coming decade will focus on this, whether translators can be in the driver's seat using machine translation as they use TMs, TBs, or dictionaries. There are more ways to use MT than just for post-editing. We just need to have some patience to wait for the next wave. The same way we did with CAT tools and other tools and environments that - in spite of the misconception - rather than competing or replacing them, meant to help and support the translators' jobs.
FreeCodeCamp (March 2018). A history of machine translation from the Cold War to deep learning. Retrieved from https://www.freecodecamp.org/news/a-history-of-machine-translation-from-the-cold-war-to-deep-learning-
Jost Zetzsche (2019 August). Fake News. The ATA Chronicle | July/August 2019
Jost Zetzsche (2019 May). Using Neural Machine Translation Beyond Post-Editing. The ATA Chronicle | May/June 2019
Jost Zetzsche (2020 February). Like two porcupines making love - VERY CAREFULLY! The ATA Chronicle | January/February 2020
Jost Zetzsche (2019 March). Artificial Intelligence and Translation Technology. The ATA Chronicle | March/April 2019