Theoretically, one could have very specific knowledge of Chinese, possibly acquired from very limited but deep experience. Imagine one person who has spoken Chinese only at the harbor, and has complete and total mastery of the maritime vocabulary of Chinese but would lack all but the simplest verbs relevant to the conversations happening just a mile further inland. Conceivably, a series of experts in a very localized domain could separately contribute their understanding, perhaps governed by a person who understands (in English) every conceivable key to the GLUT, but does not understand the values which must be placed in it.
This does not pass the simplest plausibility test. Do you imagine that being at a harbor causes people to have only conversations which are uniquely applicable to harbor activities? Does one not need words and phrases for concepts like “person”, “weather”, “hello”, “food”, “where”, “friend”, “tomorrow”, “city”, “want”, etc., not to mention rules of Chinese grammar and syntax? Such a “harbor-only” Chinese speaker may lack certain specific vocabulary, but he certainly will not lack a general understanding of Chinese.
Your other example is even sillier, especially given that the number of possible conversations in a human language is infinite. For one thing, a conversation where one person is constantly asking “Does my reply make sense?” is very, very different from the “same” conversation without such constant verbal monitoring. (Not to mention the specific fact that your imaginary expert would not be able to understand his interlocutor’s response to his question about whether his utterances made sense.)
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.
This does not pass the simplest plausibility test. Do you imagine that being at a harbor causes people to have only conversations which are uniquely applicable to harbor activities? Does one not need words and phrases for concepts like “person”, “weather”, “hello”, “food”, “where”, “friend”, “tomorrow”, “city”, “want”, etc., not to mention rules of Chinese grammar and syntax? Such a “harbor-only” Chinese speaker may lack certain specific vocabulary, but he certainly will not lack a general understanding of Chinese.
Your other example is even sillier, especially given that the number of possible conversations in a human language is infinite. For one thing, a conversation where one person is constantly asking “Does my reply make sense?” is very, very different from the “same” conversation without such constant verbal monitoring. (Not to mention the specific fact that your imaginary expert would not be able to understand his interlocutor’s response to his question about whether his utterances made sense.)
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.