Why Quality Estimation Is The Missing Link For Machine Translation Adoption

While there have been several key developments in machine translation (MT) in recent years, MT has not yet reached the level where businesses might be confident to allow it to proceed unchecked by humans. There is a paradox insofar that we want to allow artificial intelligence (AI) and automation to take on more and more tasks to relieve pressure on the human workforce, but, in turn, this creates more work for humans in terms of supervising their digital colleagues. We need look no further than the restaurant in China called "Translate Server Error" or Hillary Clinton's gift to the Russian foreign minister that was inscribed with a message that was supposed to say "reset" in Russian but actually showed the word "overcharge." AI still commits fundamental errors that are embarrassing at best, and at worst, they can convey offensive and/or completely unintended meanings. This is where the importance of quality estimation comes to the fore. A good definition of quality estimation comes from eBay, an enthusiastic user of QE: "A method used to automatically provide a quality indication for machine translation output without depending on human reference translations.