5 ms·

> Yes, it's hard to figure out cow just from a big corpus; though you might learn enough about 'cow' to form meaningful sentences and answer questions. That's w

by rvense 5y ago

> Yes, it's hard to figure out cow just from a big corpus; though you might learn enough about 'cow' to form meaningful sentences and answer questions. That's what GPT-3 is doing after all, and language models are still improving quickly. As I've said in a sibling comment, GPT-3 is able to construct new utterances in the same language that it learns from. It does not learn its language while learning a model of the world because it does not exist in the world, so it does not learn semantics like we do, which is requirement for making a translation. I am familiar with all these things you talk about, but you are not starting from a sound understanding of semiotics and the difference between content and expression. Say you learn a lot of syntax from text, you are able to write out a list of paradigms. How do you know which verb form is past and which form is present? How do you know which word means day and which word means night? The information required to make that judgement is not present in the text artifact.

eru 5y ago
> How do you know which verb form is past and which form is present? There's lots and lots of context. One interpretation will fit the data much better than another. Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_. I agree that all these connections are only probabilistic and you need enormous amounts of data. Btw, GTP-3 can translate between English and French to certain very limited extent; but that's probably mostly because it has seen example translations. To give you a testable prediction of my theory: if you trained something like GPT-3 on a corpus that includes examples of natural languages A and B, but never any piece of text that contains both languages, yet alone example translations, I would expect that nevertheless, the resulting network would share the same internal representation for the concept of 'cow' in both languages.
- rvense 5y ago
  > Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_. This still rests on a lot of assumptions about the language. Pronouns are used much more in some languages than others, some languages have more pronouns than others, etc.. Tense is not at all universal in the world's languages as you probably know. There are linguistic theories that even question the universality of nouns and verbs as categories, and even if they are to an extent universal, they are certainly not fixed in their boundaries across languages, e.g. most things that in English adjectives are expressed as verbs in Classical Arabic. Even if you could do a perfect job of extracting a set of syntactic categories and morphological paradigms from a language, I don't think you would be able to do anything with it. Word 3849 from category C often combines with words 201, 635, and 9913 from category F in conjugations 1-4, but never 5. And of course, even for nouns that refer to basic, physical things, often some languages will have several words where another will make do with one, or have none. It quickly becomes a question of culture.

by rvense 5y ago

eru 5y ago

> How do you know which verb form is past and which form is present? There's lots and lots of context. One interpretation will fit the data much better than another. Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_. I agree that all these connections are only probabilistic and you need enormous amounts of data. Btw, GTP-3 can translate between English and French to certain very limited extent; but that's probably mostly because it has seen example translations. To give you a testable prediction of my theory: if you trained something like GPT-3 on a corpus that includes examples of natural languages A and B, but never any piece of text that contains both languages, yet alone example translations, I would expect that nevertheless, the resulting network would share the same internal representation for the concept of 'cow' in both languages.

rvense 5y ago
> Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_. This still rests on a lot of assumptions about the language. Pronouns are used much more in some languages than others, some languages have more pronouns than others, etc.. Tense is not at all universal in the world's languages as you probably know. There are linguistic theories that even question the universality of nouns and verbs as categories, and even if they are to an extent universal, they are certainly not fixed in their boundaries across languages, e.g. most things that in English adjectives are expressed as verbs in Classical Arabic. Even if you could do a perfect job of extracting a set of syntactic categories and morphological paradigms from a language, I don't think you would be able to do anything with it. Word 3849 from category C often combines with words 201, 635, and 9913 from category F in conjugations 1-4, but never 5. And of course, even for nouns that refer to basic, physical things, often some languages will have several words where another will make do with one, or have none. It quickly becomes a question of culture.