4 ms·
> It's my understanding that machine translation started to become semi-useful after abandoning traditional grammars...and switched to statistics. What's curio
by cr0sh 7y ago
> It's my understanding that machine translation started to become semi-useful after abandoning traditional grammars...and switched to statistics.
What's curious about this statement (and forgive me if I am in error here; I am not extremely familiar with the machine translation field) is that the community chose to go down the route of "traditional grammars" and their rules, for the purposes of translation, rather than down the route of statistics to begin with.
Because as it was noted, this happened after WW2 and the advances in cryptography, which were by and large also driven by statistical analysis and other similar mathematically based advances; one would think that such insights would have carried over into the machine translation realm.
But they didn't - which is simply a curious historical footnote. It's also something we see often in history - particularly as it relates to technological advances; that there are certain paths that are taken that lead to virtual dead-ends decades later, but which the path that should have been taken was presaged by earlier work, yet for one reason or another wasn't pursued.
If we only had a way to avoid such "wrong turns", we could be much further along technologically - but of course, that would also be tantamount to "telling the future"...almost.
- mcguire 7y agoThe downside of statistical models has always been understanding and explaining why they do whatever they do. Any statistical model will generate a result, either right or wrong, but it's impracticable to figure out how it came up with that result: the only knob you have is training data. "Symbolic" techniques are the usual first choice because in order to get them to work reasonably well (i.e. beyond the 60%-80% threshold), you have to understand what they're doing.