Translation apps are getting better, but they're still not perfect, particularly for minority languages. Can artificial intelligence and deep neural networks help iron out the glitches?
During the World Cup in Russia this summer there was a dramatic spike in the use of Google Translate, the company says, as fans tried to strike up conversations with their hosts and fellow fans from around the world.
The words for "stadium" and "beer" were in particularly high demand.
These days the traditional phrasebook is on the way out. A recent survey from the British Council found that nearly two-thirds of 16 to 34-year-olds now rely on translation apps to help navigate the local lingo.
But while such apps are undoubtedly getting better, they're still not totally reliable - a fifth of those surveyed said they experienced misunderstandings while on holiday because of mistranslations on their phone.
The issue is particularly acute for speakers of non-mainstream languages.
Welsh people, for example, have been noticing some particularly "scummy" translations. One warning sign reading "Blasting in Progress" was rendered as "Gweithwyr yn ffrwydro" or "workers exploding", for example.
And this summer, a Google Translate user discovered that typing "dog" 18 times produced a Maori translation reading: "Doomsday Clock is three minutes at twelve We are experiencing characters and a dramatic developments in the world, which indicate that we are increasingly approaching the end times and Jesus' return."
So why are translations glitches still happening in the age of supercomputers and machine learning?
One big problem is that words often have more than one meaning. These homographs, as they're called, can lead to embarrassment not just for holidaymakers but for governments as well.
Take the UK government's botched German version of the Brexit white paper in July, which translated the phrase "democratic exercise" as "demokratische Übung" - where "Übung" meant physical exercise not practice.
To deal with mistakes like this, translation apps are continuously refining the ways in which machine learning is applied. They make use of previously translated texts to provide their answers, checking the context in which the word has been used before and selecting the most likely meaning.