A more realistic version would be for for an observer to record all conversations between two Chinese speakers with length N, where N is some arbitrarily large but still finite conversation length. (If a GLUT were to capture every possible conversation, you are correct in saying that it would have to be infinite).
From a sufficiently large sample size (though it is implausible to capture every probable conversation in any realistic amount of time, not to mention in any amount of time during which the language is relatively stable and unchanging), a tree of conversations could be built, with an arbitrarily large probability of including a given conversation within it.
From this, one could built a GLUT (though it would probably be more efficient as a tree) of the possible questions given context and the appropriate responses. Though it would be utterly unfeasible to build, that is a limitation of the availability of data, rather than the GLUT structure itself. It would not be perfect—one cannot build an infinite GLUT, nor can one acquire the infinite amount of data with which to fill it—but it could, perhaps, surpass even a native speaker by some measures.
Consider: what would the table contain as appropriate responses for the following questions? (Each question would certainly appear many, many times in our record of all conversations up to length N.)
“Hello, what is your name?”
“Where do you live?”
“What do you look like?”
“Tell me about your favorite television show.”
Remember that a GLUT, by definition, matches each input to one output. If you have to algorithmically consider context, whether environmental (what year is it? where are we?), personal (who am I?), or conversation history (what’s been said up to this point?), then that is not a GLUT, it is a program. You can of course convert any program that deterministically gives output for given input into a GLUT, but to do that successfully, you really do need all possible inputs and their outputs; and “input” here means “question, plus conversation history, plus complete description of world-state” (complete because we don’t know what context we’ll need in order to give an appropriate response).
In other words, to construct such a GLUT, you would have to be well-nigh omniscient. But, admittedly, you would not then have to “know” any Chinese.
You make some valid points.
A more realistic version would be for for an observer to record all conversations between two Chinese speakers with length N, where N is some arbitrarily large but still finite conversation length. (If a GLUT were to capture every possible conversation, you are correct in saying that it would have to be infinite).
From a sufficiently large sample size (though it is implausible to capture every probable conversation in any realistic amount of time, not to mention in any amount of time during which the language is relatively stable and unchanging), a tree of conversations could be built, with an arbitrarily large probability of including a given conversation within it.
From this, one could built a GLUT (though it would probably be more efficient as a tree) of the possible questions given context and the appropriate responses. Though it would be utterly unfeasible to build, that is a limitation of the availability of data, rather than the GLUT structure itself. It would not be perfect—one cannot build an infinite GLUT, nor can one acquire the infinite amount of data with which to fill it—but it could, perhaps, surpass even a native speaker by some measures.
I remain dubious.
Consider: what would the table contain as appropriate responses for the following questions? (Each question would certainly appear many, many times in our record of all conversations up to length N.)
“Hello, what is your name?”
“Where do you live?”
“What do you look like?”
“Tell me about your favorite television show.”
Remember that a GLUT, by definition, matches each input to one output. If you have to algorithmically consider context, whether environmental (what year is it? where are we?), personal (who am I?), or conversation history (what’s been said up to this point?), then that is not a GLUT, it is a program. You can of course convert any program that deterministically gives output for given input into a GLUT, but to do that successfully, you really do need all possible inputs and their outputs; and “input” here means “question, plus conversation history, plus complete description of world-state” (complete because we don’t know what context we’ll need in order to give an appropriate response).
In other words, to construct such a GLUT, you would have to be well-nigh omniscient. But, admittedly, you would not then have to “know” any Chinese.