4 ms·

Yes! As somebody who speaks 2 languages, and sort of reads/understands 2 more, I cannot agree more. Human spoken languages do not follow any grammars. Grammars

Yes! As somebody who speaks 2 languages, and sort of reads/understands 2 more, I cannot agree more. Human spoken languages do not follow any grammars. Grammars are just simplified representations of reality that is probabilistic in nature. This is something that Chomsky got very wrong, and the statistical/ML crowd got very right. But still, grammars are a very useful model.

pessimizer 2y ago
Languages definitely follow grammars. They don't follow the grammars that were written by observing them, but you can discover unwritten grammatical structures that are nevertheless followed by everyone who speaks a language, and who if asked wouldn't even be able to articulate the rules that they are following. It's the following that defines the grammar, not the articulation of the rules. Statistics are just another way to record a grammar, all the way down to the detail of how one talks about bicycles, or the Dirty War in Argentina. If a grammar is defined as a book that enumerates the rules of a language, then of course language doesn't require following a grammar. If a grammar is defined as a set of rules for communicating reasonably well with another person who knows those same rules, then language follows grammars.
- vkazanov 2y ago
  > Languages definitely follow grammars But it's the other way around! Grammars follow languages. Or, more precisely, grammars are (very lossy) language models. They describe typical expectations of an average language speaker. Grammars try to provide a generalized system describing an average case. I prefer to think of languages as a set of typical idioms used by most language users. A given grammar is an attempt to catch similarities between idioms within the set and turn 'em into a formal description. A grammar might help with studying a language, and speed up the process of internalizing idioms, but the final learning stage is a set of things students use in certain situations aka idioms. And that's it. > Statistics are just another way to record a grammar I almost agree. But it should be "record a language". These are two approaches to the problem of modeling human languages. Grammars are an OK model. Statistical models are less useful to us humans but given the right amount of compute they do show much better (see LLMs).
  - canjobear 2y ago
    This is a terminological difference. Linguists use "grammar" as a technical term for a speaker's implicit knowledge of how their language works. That knowledge could be statistical or rule-based in nature, although most linguistic theories say that it's rule-based. You're using grammars to mean human-produced descriptions of that knowledge.
    - vkazanov 2y ago
      That's correct. Grammars the way I understand them are are a family of human language models. Typically discrete in nature. The approach was born out of Chomsky's research culminating in the Universal Grammar idea.
  - foobarqux 2y ago
    This is just wrong. Languages follow certain inviolable rules, most notably, hierarchical structure dependence. There are experiments (Moro, the subject "Chris") that show that humans don't process synthetic languages that violate these rules the same as synthetic languages that do (specifically it takes them longer to process and they use non-language parts of the brain to do so).
    - vkazanov 2y ago
      What exactly is wrong? The fact that grammars are very limited models of human languages? My key thesis is that human languages operate in a way that non-probabilistic models (i.e. grammars) can only describe it in a very lossy way. Sure, LLMs are also lossy but also much more scalable. I've spent quite a lot of time with 90s/2000s papers on the topic, and I don't remember any model useful in generating human language better than "stohastic parrots" do.
      - foobarqux 2y ago
        As I said there are universal rules that human language processing follows (like hierarchical structure dependence); you can't have arbitrary syntax/grammars. It's true that science hasn't solved the main puzzles about how to characterize these rules. The fact that statistical models are better predictors than the-"true"-characterization-that-we-haven't-figured-out-yet is completely irrelevant, just as it would be irrelevant if your deep-learning net was a better predictor of the weather: it wouldn't imply that the weather doesn't follow rules in physics, regardless of whether we knew what those rules were.

pessimizer 2y ago

Languages definitely follow grammars. They don't follow the grammars that were written by observing them, but you can discover unwritten grammatical structures that are nevertheless followed by everyone who speaks a language, and who if asked wouldn't even be able to articulate the rules that they are following. It's the following that defines the grammar, not the articulation of the rules. Statistics are just another way to record a grammar, all the way down to the detail of how one talks about bicycles, or the Dirty War in Argentina. If a grammar is defined as a book that enumerates the rules of a language, then of course language doesn't require following a grammar. If a grammar is defined as a set of rules for communicating reasonably well with another person who knows those same rules, then language follows grammars.

vkazanov 2y ago
> Languages definitely follow grammars But it's the other way around! Grammars follow languages. Or, more precisely, grammars are (very lossy) language models. They describe typical expectations of an average language speaker. Grammars try to provide a generalized system describing an average case. I prefer to think of languages as a set of typical idioms used by most language users. A given grammar is an attempt to catch similarities between idioms within the set and turn 'em into a formal description. A grammar might help with studying a language, and speed up the process of internalizing idioms, but the final learning stage is a set of things students use in certain situations aka idioms. And that's it. > Statistics are just another way to record a grammar I almost agree. But it should be "record a language". These are two approaches to the problem of modeling human languages. Grammars are an OK model. Statistical models are less useful to us humans but given the right amount of compute they do show much better (see LLMs).
- canjobear 2y ago
  This is a terminological difference. Linguists use "grammar" as a technical term for a speaker's implicit knowledge of how their language works. That knowledge could be statistical or rule-based in nature, although most linguistic theories say that it's rule-based. You're using grammars to mean human-produced descriptions of that knowledge.
  - vkazanov 2y ago
    That's correct. Grammars the way I understand them are are a family of human language models. Typically discrete in nature. The approach was born out of Chomsky's research culminating in the Universal Grammar idea.
- foobarqux 2y ago
  This is just wrong. Languages follow certain inviolable rules, most notably, hierarchical structure dependence. There are experiments (Moro, the subject "Chris") that show that humans don't process synthetic languages that violate these rules the same as synthetic languages that do (specifically it takes them longer to process and they use non-language parts of the brain to do so).
  - vkazanov 2y ago
    What exactly is wrong? The fact that grammars are very limited models of human languages? My key thesis is that human languages operate in a way that non-probabilistic models (i.e. grammars) can only describe it in a very lossy way. Sure, LLMs are also lossy but also much more scalable. I've spent quite a lot of time with 90s/2000s papers on the topic, and I don't remember any model useful in generating human language better than "stohastic parrots" do.
    - foobarqux 2y ago
      As I said there are universal rules that human language processing follows (like hierarchical structure dependence); you can't have arbitrary syntax/grammars. It's true that science hasn't solved the main puzzles about how to characterize these rules. The fact that statistical models are better predictors than the-"true"-characterization-that-we-haven't-figured-out-yet is completely irrelevant, just as it would be irrelevant if your deep-learning net was a better predictor of the weather: it wouldn't imply that the weather doesn't follow rules in physics, regardless of whether we knew what those rules were.