In early 2017, became one of the first companies to experiment with Neural Machine Translation (NMT).

Innovation has always been the driving force behind’s success, so this was hardly a surprise. But for people like me, who translate and localise for a living, the news also opened a pandora’s box of eventualities where my job is replaced by a bot.

Language Specialists don’t depend on knowledge alone to translate and localise content. We use data too. Using translation memories, we can translate in a context-dependent way that suits the local market and the customer (which is the key difference between translation and localisation).

The use of data in the form of translation memories sounds pretty benign compared to Neural Machine Translation. I recently attended a conference organised for the Data Science community at Nishikant Dhanuka, a Senior Data Scientist leading the NMT team, gave a talk on data-driven Machine Learning. I hoped it would finally shed some light on the conundrum of Language Specialist vs. Machine Translation.


Neural Machine Translation at

At, property descriptions, room descriptions and hotel names are translated by freelancers across 43 languages. These freelancers use Google’s Translation Toolkit, which runs on the Statistical Machine Translation (SMT) method. For Turkish, SMT is no better than a toaster at translating creative texts, whereas NMT is more like Ava from Ex Machina, scarily close to a human being. Let me explain why.

SMT works by breaking up the source sentence into parts and then translates these on a phrase-by-phrase basis. For English-Turkish translations, the result of SMT-produced texts is usually incorrect, and sometimes hilarious. Imagine reading about frying fish in the middle of a sentence about someone’s career growth. That’s how Google translates the English idiom “to have bigger fish to fry” into Turkish. This is where SMT falls way behind humans’ ability to understand context. As a result, SMT-produced texts need heavy editing by freelancers before they become readable.

Turkish has recently become one of the languages to be tested with NMT. The process requires a combination of automated and human evaluation of the NMT-translated text to feed the algorithm’s deep learning. For the human part, my team’s help was needed. I was confident that NMT would suffer the same fate as SMT texts, but the results proved otherwise. With only a few exceptions, the texts looked like they were the work of a human linguist.


Doomsday Scenarios

One of the first questions asked after the NMT presentation was “When will these algorithms make our jobs obsolete?” Hearing this concern raised by someone in Tech only validated my fears. But Dhanuka was quick to state that NMT will only be used for less creative texts and that humans will always be part of the process.

My fear of being replaced by technology made it hard to see that these algorithms could do the routine parts of my work for me – letting me focus on more creative and rewarding translations.

The trouble I had accepting this new technology reminded me of an interview with Kevin Kelly, the founding editor of WIRED and a philosopher of technology. Kelly explains how the technology of the last decade has moved from being a useful tool to being very close to our own being. This begs the question “Who are we?” – and the same applies when it comes to NMT. If these technologies are inching closer to doing the work of a human linguist, then what does my job consist of?

This question has an existential quality to it, and that might be why it’s scary. But I can’t find answers by shying away from technology. Technology evolves with us, whether we want it to or not. My skepticism doesn’t make NMT go away — it only makes it difficult for me to see where I fit into the picture.

As a Language Specialist working to deliver the best localised product, it’s necessary to redefine my role within these evolving technologies. The only way I can do that is by diving deep and getting to know them better.


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