In a recent NPR news piece Uber Plans To Kill Surge Pricing, Though Drivers Say It Makes Job Worth It, Jeff Schneider, engineering lead at Uber describes how they are using machine learning to hack the problem of supply and demand.
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I was recently involved in a project to clean up TM pollution, where the target included a lot of translations from a different language variant. I had to work with the internal linguist to prepare a plan to do it in the most efficient way. No matter how many automation tricks I pulled from my hat, the linguist had reservations for them all and it seemed that, quality assurance-wise, nothing could beat running content through a pair of eyes. And yet the cost of manual review was prohibitive; plus one could also argue about the effectiveness of this approach, considering the amount of errors that keep showing up at every stage of a typical translation workflow, where manual review is present everywhere: translation-editing-proofing, client validation, QA/testing.
The question is: are we all on the same evolutionary path? Or do some of us take different turns, make shortcuts and even arrive in different places? The TAUS Executive Forum on April 25 and 26 in Beijing opened new perspectives on translation in China that many of us had perhaps not expected. China typically copies what others have already built and done before them. Fast trains are modeled to the TGV in France, electric cars are inspired by Tesla, and fashion in China follows the trends around the world closely.
Skype conversations can be translated in real time. Booking site reviews can be automatically translated depending on your language preference. Business emails written in Japanese can be rendered in French in just a couple of seconds. If you cannot read street signs in Tbilisi, Georgia, just take out your smartphone and it will guide you to your destination in whichever language you choose. Language barriers should not stop you anymore, modern technology is here to help!
Over the last two decades our industry has spent a lot of time and energy making the localization process increasingly more efficient. Dollars spent in the process are highly leveraged thanks to the application of translation memories and machine translation, together with sophisticated expert systems and static analysis tools that help ensure that quality can still be achieved at scale. Companies that release products internationally know how to write global-ready code and content, standards exist and are generally adhered to. Companies wishing to extend into international markets for the first time have a deep bench of partners and suppliers that they can rely on to get the job done. Localizing products is not always a trivial task but it is one that is well understood by a mature industry.
On 15 March, the Imperial Riding School Renaissance Hotel in Vienna was transformed into a battlefield of ideas on new trends and topics related to translation automation during a TAUS Roundtable meeting. The Roundtable was organized around questions on the market viability of translation data, on innovative ways of measuring localization effectiveness and on machine translation quality.
* The title is borrowed from an article written by Bill Joy (then Chief Scientist at Sun Microsystems) and published in Wired Magazine in April 2000. (Why the future does not need us). This article was somewhat gloomy, giving us a warning about a future in which machines essentially dominate us, humans. “We must do more thinking up front if we are not to be (…) surprised and shocked by the consequences of our inventions.” Projecting this fundamental and existential problem on our own sector, the field of translation, could easily lead to depressing and devastating visions of the translation industry in the coming decades, and as a result put us – everyone working in this industry – in a defensive and reactive or inactive state of mind. What we much rather do is be realistic about it, have an open mind about both the upsides and the downsides. The future may not need us, but we certainly need a future.
Last year, I did some New Year's predictions in my post Wishing you an innovative 2015! reflecting on topics that would keep us awake in 2015. To continue writing on these expectations (calling them predictions might be too pretentious), making this a tradition, here is my two cents again on some of the new topics that will dominate this year’s blog posts and translation conferences. Last time, I talked about big data and business intelligence becoming the buzz words of the year. I also mentioned a new direction in the industry towards dynamic pricing based on quality and productivity results. And of course, translation quality will be a popular discussion topic everywhere you go. Just read on to see why...