An anniversary year
2016 marks the fiftieth anniversary of an important event in the history of Machine Translation (MT). In 1966, after two years of work, the group of seven scientists who constituted the US National Science Foundation’s Automatic Language Processing Advisory Committee (ALPAC) handed down a 124-page report that was, well, somewhat negative about the state of MT research and its prospects. The ALPAC report is widely credited with causing the US government to drastically reduce funding in MT, and other countries to follow suit.
As it happens, 2016 also marks the tenth anniversary of the launch of the Google Translate web-based translation service, which was soon followed in 2007 by Microsoft’s Translator. Google says its translation service is used more than a billion times a day worldwide, by more than 500 million people a month. In mid-2015, one market research report estimated that, by 2020, the global MT market will be worth $10B.
Not a bad turnaround in outlook, even if it did take a few decades.
MT is special
In the portfolio of language technology applications that are the focus of interest of this journal’s readership, MT occupies a special place. MT was the goal of one of the very first experiments in Natural Language Processing. In 1954, the Georgetown–IBM MT system automatically translated sixty Russian sentences into English, leading its authors to claim that within three or five years, MT might be a solved problem. You can still find the original press release on the web; it’s a fascinating read, with its detailed description of a ‘brain’ that ‘dashed off its English translations . . . at the breakneck speed of two and a half lines per second.’
MT is also special because it’s one of the first areas of Natural Language Processing where statistical methods took hold in a big way. Although the idea of statistical MT was first raised by Warren Weaver in a 1949 memorandum, it was IBM’s influential statistical MT work in the late 1980s and early 1990s that caused researchers to sit up and take notice. I think it’s reasonable to claim that the perceived successes of Statistical Machine Translation (SMT) have been a major driver for the application of statistical techniques in other areas of Natural Language Processing since that time.
And MT is special because it’s possibly the most accessible form of language technology in terms of the popular understanding. It can be a struggle to explain to the layperson exactly what text analytics is, or why it is that grammar checkers and speech recognisers make mistakes. But most people get what MT is about, and can see that it might be a hard thing to do; many people have struggled with learning a second language. Nobody doubts the value of a technology that can take one human language as input and provide another as output.
In fact, universal translators have been a staple of science fiction, and thus part of the popular imagination, since at least 1945. Devices that can translate languages have played a role in many popular sci-fi TV shows. You can even guess someone’s age bracket by the movie or TV show whose name comes to mind when you mention the idea—for me, it’s Star Trek, where the back-story is that the Universal Translator was first used in the late twenty-second century for the translation of well-known Earth languages.
From where we stand now, Star Trek’s creator, Gene Rodenberry, looks to have been just a bit on the cautious side with his predictions. Perhaps he had read the ALPAC report: the Universal Translator first showed up in a 1967 episode of the show.
In the rest of the article, Robert Dale looks at where we are now, MT delivery models, considers humans versus machines gives his opinion on where the commercial potential lies.
Read the article ‘How to make money in the translation business’
You may also be interested in complimentary access* to a collection of related articles on Machine Translation from the journal Natural Language Engineering. *Free access available until 31 March 2016