Sunday, October 16, 2016

The most interesting part of Andrew Carnie’s “Introduction to Syntax” for me was his discussion of Universal Grammar and different theories about language acquisition. It seems natural that, amidst Carnie’s discussion of the immense efforts linguists have made to scientifically pin down the syntactical rules of language, how all of us somehow naturally manage to accomplish this effortlessly in the relatively short amount of time it takes us to learn how to speak. As Carnie establishes midway through the first chapter, language has a recursive structure that allows it to create infinite permutations, which makes the task of extrapolating syntax rules from language around us a daunting one in terms of size. In response to the idea that the language data set we as children are exposed to is too small to allow us to adequately grasp these rules, Noam Chomsky’s theory of Universal Grammar posits that there is an innate human facility for language--that to some extend, these rules are innate to us.
What I found most interesting about this idea was its implications for machine learning and natural language processing. In Symsys1, we talked a bit about the recursive structure of language and how ‘context free grammars’ create recursive, hierarchical syntax trees to generate input. Given the amount of overlap between the two classes, I started thinking a bit about the problems each raised for the other. If Universal Grammar is right in positing that our ability to learn language is dependent on some innate facility, would it be possible to ‘teach’ a computer that facility, or is it something dependent on our biology (and can those structures be reproduced within a machine)? If neither is the case, what does that imply about the nature of language? From the other end, thinking about all the efforts computer scientists have hitherto made to attempt to get machines to simulate real speech made me think about what insights computer science could bring to linguistics. Could attempts to teach computers language shed light on different models for language acquisition in children?

3 comments:

  1. I was recently surprised by the similarities between my CS 106B class and Linguistics 1, and this post reaffirms the eerie commonalities. In CS right now, we are learning about recursion and how we can break up bigger problems into smaller iterations of the problem. Our instructor used trees and fractal trees to explain the rules of recursion to our class, and it is exciting to see these same trees emerging in the study of Language.

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  3. Going off of your point about "teaching" a computer grammar, it seems to me that the biggest difficulty with getting computers to completely understand and produce speech is going to be one of efficiency. Of course, coding the rules of grammar will be way more efficient than coding an infinite lexicon of sentences -- but these rules are complicated and intricate, without getting into the other complicated intricacies of casual human speech, and the human brain is hard-wired for it in ways that a computer is not.

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