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> When Hofstadter says "There's no fundamental reason that machines might not someday succeed smashingly in translating jokes, puns, screenplays, novels, poems, and, of course, essays like this one. But all that will come about only when machines are as filled with ideas, emotions, and experiences as human beings are", that is just an assertion. I can translate passages about war even though I've never been in a war. I can translate a novel written by a woman even though I'm not a woman. When refuting his claims, he also makes some errors. Hofstadter has some very good reasons to justify what he says, whereas Shallit's argument is more or less "we can do slightly better translations now than before, so there is no reason they couldn't be better" - whereas the whole point of Hofstadter is that it's impossible to do exactly using current methods. I understand both views and I think Shallit might have a point, but he doesn't justify it well. Simply by using statistical methods you can achieve surprisingly good results, although the error ratio is still too high IMO. What we already can do is to produce relatively good translations of texts belonging to well defined domains, such as legal documents. But in order to do it well with all domains, we'd need to find a good method of passing on the necessary context information to the translation engine. Imagine translating subtitles of a movie. It's absurd to expect the machine will produce better results than a human as it's lacking visual cues. However, if we manage to transmit this information to the translation machine (via Computer Vision, audio profiling etc.), it can get much better results. It's very difficult to expect good results could be obtained just by training the neural networks based on previous movies. Yet, this is what Shallit seems to argue. |