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Discussion Details




Title: Remarks by Noam Chomsky in London
Submitter: Geoff Pullum
Description: About a month ago (10 October 2011) Noam Chomsky spoke at an
invitation-only seminar at University College London (UCL). I attended
along with about 90 other British linguists. The announced title was:
"On the poverty of the stimulus". The video of both the talk and the
question period is available:



(http://www.ucl.ac.uk/psychlangsci/research/linguistics/news-events/latest-news/n_chomsky; henceforth, UCL video).



In what follows I summarize some of the content of Chomsky's London
talk and its question session, and explain some of my reactions.



Chomsky's remarks in London were not very different in tone from
things he has said elsewhere: the UCL presentation was extremely
similar to a lecture given at Carleton University in Canada last April
(http://www.youtube.com/watch?v=XbjVMq0k3uc), and echoed themes
from Chomsky's talk at the symposium on the biology of language at
the 2011 Cognitive Science Society conference in Boston last July, and
journal articles such as "Language and other cognitive systems"
(Chomsky 2011), and particularly the paper "Poverty of the stimulus
revisited" (Berwick et al. 2011, henceforth BPYC-2011). These recent
talks and papers share a steadfast refusal to engage with anything that
might make the debate about the poverty of the stimulus (POS) an
empirical one. They issue blanket dismissals of nearly all modern
cognitive/linguistic science as worthless, and sweep aside whole
genres of work on the basis of what seems to be extremely shallow
acquaintance. Claims about parallels in the natural sciences feature
prominently, as does a preference for authority over evidence. I will
discuss a selection of topics, without attempting to be very systematic.



1. Rocks and kittens



Two aspects of the way Chomsky chose to deal with the topic of
stimulus poverty struck me as startling. The first was that he stuck
entirely with the version of the argument from POS that the late
Barbara Scholz used to call the rocks-and-kittens version.



A child's pet kitten (so the argument goes), exposed to the same
primary linguistic data as the child, learns no language at all, and is
indistinguishable from a rock in this regard. Since the linguistic inputs
are the same, an innate interspecies difference in language readiness
and capacity for language acquisition must be involved; therefore
linguistic nativism is true. (This is not parody, as I scarcely need to
document: Chomsky has happily repeated his views on kittens and the
like many times. A Google search on a pattern as specific as
{Chomsky granddaughter rock kitten innate} will yield tens of
thousands of hits, nearly all relevant ones. See Smith 1999: 169-170,
or Stemmer 1999, or Chomsky 2000: 50 for quotable quotes in print.)



At UCL Chomsky didn't really give even this much of an argument: he
just noted that humans had a genetic endowment that permitted them
to learn language, and stipulated that he would call it Universal
Grammar (UG). (Compare, e.g., "The faculty of language then is a
special property that enables my granddaughter but not her pet kitten
or chimpanzee to attain a specific I-language on exposure to
appropriate data..."
(http://hotbookworm.wordpress.com/2010/01/11/noam-chomsky-the-biolinguistic-turn-lecture-notes-part-two/.)



He even admitted that "intellectually ... there's just nothing to it --- [it's]
a truism" (UCL video, 3:42); but he went on to argue that there is "a
kind of pathology in the cognitive sciences" (UCL video, 4:24) in that its
practitioners obdurately refuse to accept the simple point involved.



The real trouble, of course, is that everyone accepts it --- nobody
doubts that there is something special about humans as opposed to
kittens and rocks --- but they do not recognize it as a scientific result
concerning human beings or their capacities.



What I had imagined would be under discussion in this seminar is the
specific view about the character of human first language acquisition
that is known as linguistic nativism. This is a substantive thesis
asserting that language acquisition is largely guided by an intricate,
complex, human-specific, internal mechanism that is (crucially)
independent of general cognitive developmental capacities. This
assertion seems to me worthy of serious and lengthy discussion. The
rocks-and-kittens claim is surely not. We all agree that kittens and
rocks can't acquire language, and that it's not because they don't get
sufficient exposure. But that hardly amounts to support for linguistic
nativism over general nativism (Scholz & Pullum 2002: 189).



It's not that Chomsky doesn't recognize the distinction between
linguistic nativism and general nativism. He says (Chomsky 2000: 50,
reproduced at (http://www.chomsky.info/books/architecture01.htm):



''Now a question that could be asked is whether whatever is
innate about language is specific to the language faculty or
whether it is just some combination of the other aspects of
the mind. That is an empirical question and there is no reason
to be dogmatic about it; you look and you see. What we seem to
find is that it is specific.''



But to say that you simply look and see, when the question is as subtle
and difficult as this one and concerns mechanisms inaccessible to the
tools we currently have, is surely not a responsible characterization of
what science involves.



2. Stimulus poverty without the stimulus



The second striking choice Chomsky made was to address the poverty
of the stimulus without ever mentioning the stimulus at all. This was
POS without the S. One would expect that when someone claims that
the child's input is too poverty-stricken to support language acquisition
through ordinary learning from experience, they would treat empirical
observations about the nature of that input as potentially relevant. It
would give a POS argument some empirical bite if one could specify
ways in which the child's input was demonstrably too thin to support
learning of particular features of language from experience of
language use. That would seem worthy of attention. The rocks-and-
kittens version does not. I was very surprised that Chomsky stuck to it
so firmly (though that does explain his lack of interest in the child's
input: the rocks-and-kittens argument doesn't need anything to be true
or false of the input).



The POS issue is going to take a long time to resolve if we can't even
focus on roughly similar versions of the purported argument. Yet
Chomsky regards it as crucial that it be resolved. He began his talk, in
fact, with some alarmist remarks about the prospects for linguistics
("the future of the field depends on resolving it": UCL video, 4:38). If
we do not settle this question of stimulus poverty, he claimed, we are
doomed to seeing our subject shut down. So he portrays current
skepticism among cognitive scientists about linguistic nativism as not
just obtuse, but actively harmful, a threat to our whole discipline.



This is an interesting (if rather risky) new way of stoking enthusiasm for
linguistic nativism: appeal to linguists' self-interest and desire for
security (you don't want to be shut down, do you?). But it's hard to
take seriously. Linguistics is not going to die just because a fair
number of its practitioners now have at least some interest in machine
learning, evolutionary considerations, computational models of
acquisition, and properties of the child's input, and are becoming
acquainted with probability theory, corpus use, computer simulation,
and psychological experimentation --- as opposed to waving all such
techniques contemptuously aside.



3. The lesson of Bayes' Theorem



Chomsky went on to remind us all of the linguists and psychologists in
the 1950s who (allegedly) stuck so rigidly to corpus data that they
regarded experiments going beyond the corpus data as almost a
betrayal of science. And he stressed that the work of people today
who work on Bayesian learning of patterns or regularities from raw
data has no value at all ("zero results"). He compared their modeling
of phenomena to physicists making statistical models to predict the
movements of medium-sized physical objects seen outside in the
street (UCL video, 36:41).



I think such a blanket dismissal overlooks a crucial conceptual
contribution that Bayesian thinking makes to theoretical linguists, one
that has nothing to do with the statistical modeling on which Chomsky
pours such scorn. Many linguists have given the impression that they
think it is impossible to learn from positive data that something is not
grammatical. Lightfoot (1998: 585) suggests, for example, that
although you can perhaps learn from experience that auxiliary
reduction is optional in the interior of a clause, you cannot possibly
learn that it is forbidden at the end of a clause; hence linguistic
nativism has to be true. This reasoning is flawed, and Bayes' Theorem
teaches us why.



The lesson is that probability of a generalization G being correct given
a body of evidence E is not dependent merely on whether E contains
crucial evidence confirming G over its rivals. The probability of G is
proportional to the product of the antecedent probability of G's being
true with something else: the probability that the evidence would look
like E if G were true. That means that what is absent from experience
can be crucial evidence concerning what the grammar has to account
for. For example, all the thousands of times you've heard clause-final
auxiliary verbs uncontracted strengthen the probability that they're not
allowed to contract.



The argument from absence of stimulus is pretty much demolished by
this Bayesian insight: the argument form simply is not valid. And for
people who use the phrase "the logical problem of language
acquisition" (as linguistic nativists have been doing since 1981), that
ought to mean something. It certainly seems to me sufficient to justify
including at least a brief introduction to Bayesian statistical reasoning in
the education of every theoretical linguist.



Suppose, though, that it ultimately turns out that the current fashion for
constructing Bayesian computational models of learning is something
of a dead end. It still doesn't follow that it is deleterious. Much can be
learned by watching models ultimately fail. There is no threat to the
discipline here: linguistics is not so fragile that it will collapse just
because one possibly false trail was followed.



The people interested in Bayesian modeling and similar computational
lines of research are smart enough to eventually perceive its
inadequacy (if indeed it is inadequate), and will move to something that
looks more interesting. People get bored in dead-end ventures. I
talked to Roger Brown in 1968 and he told me that the reason he had
abandoned Skinnerian behaviorism ten years before had nothing to do
with any revolutionary new ideas in scientific thinking about cognition or
the impact of Chomsky's famous review of Skinner: he was just bored
with the work that behaviorism demanded, and wanted to try
something more interesting. Intellectually agile people want to move
on.



4. Bias at the NSF



About half-way through his talk, Chomsky made some claims about the
probability of success with proposals to the NSF to fund research
projects on Universal Grammar (UG). He said: "If you want a grant
from the National Science Foundation, you better not include that [the
phrase "UG"] in your proposal; it will be knocked out before it even
reaches the review board" (UCL video, 30:35).



He warmed to this theme: "If you want to get a grant approved, you
have to have the phrase 'sophisticated Bayesian' in it, and you also
have to ask for an fMRI, especially if you have nothing whatever to do
with it" (he chuckled here and there was general laughter) "... if you
meet those two conditions, you might make it through the granting
procedures" (UCL video, 31:02).



Then he returned to the claim that "UG" will doom your proposal: "But if
you use a dirty word like UG, and you say there's something special
about humans and we've got to find out what it is, that pretty much
rules it out" (UCL video, 31:18). And then, with no chuckling, he added:
"I'm not joking; I have concrete cases in mind ... of good work that just
can't get funded, because it doesn't meet these conditions... Right at
MIT in fact" (UCL video, 31:28).



Since award details are public information, it is trivial to find out
whether the NSF is making awards for purely theoretical study of UG in
a Chomskyan perspective. And it is. Željko Bošković's grant "On the
Traditional Noun Phrase: Comparing Languages With and Without
Articles" (BCS-0920888) is an example. And MIT is not left out. For
example, David Pesetsky obtained Doctoral Dissertation Research
grant no. BCS-1122426 for a project "Argument licensing and
agreement"; the abstract begins: "Which properties of human language
are universal, and which may vary across languages? Answering
these questions will help us understand the unique human capacity for
language, through which we hope to gain insight into the overall
architecture of the human mind." And Chomsky must know that his co-
author Robert Berwick received grant BCS-0951620 for a "Workshop
on Rich Grammars from Poor Inputs" at MIT in 2009.



Naturally, many NSF proposals mentioning UG will go unfunded --- the
majority, given that across the board less than 25% of grant proposals
get funded. But (of course) proposals are sent out for peer review
whether they mention UG or not, and whether they mention Bayes or
not.



It seems a strange strategy to make claims of this sort to an audience
of linguistics professionals in a foreign country who would have little
knowledge of the NSF, and send out the message to young
investigators internationally that following Chomsky's theoretical line
will blight their careers by dooming their chances of NSF funding. Even
if this were true, it would give the impression of a fractious field that
has bad relations with its most important Federal funding agency. But
it is much stranger to make such statements when they are easily
discovered to be false.



5. An uncomprehended question about machine learning



In the question period there was an extremely unfortunate interaction
when the computational learning experimenter Alexander Clark tried to
ask a question. Chomsky interrupted and began his answer before
Clark had managed to make his point. The question Clark want to put
was roughly the following (I knew enough to see where he was going,
and he has confirmed to me that this was what he meant).



A paper Clark had published with Eyraud (2007) on learning some
kinds of context-free grammars (CFGs) from positive data is dismissed
in BPYC-2011 as useless. Chomsky repeated that dismissal in his talk.
But Clark's more recent work has focused on languages in the much
larger context-sensitive family that are generated by minimalist
grammars as formalized by Edward Stabler. These are strongly
equivalent to the Multiple Context-Free Grammars (MCFGs) that were
invented by Seki & Fujii (1991), as Clark tried to begin to explain. He
was not attempting to say anything about CFGs, but to raise the issue
of learning the languages of minimalist grammars, or equivalently
MCFGs. This is a wildly different class, vastly larger than the class of
CFGs. It corresponds to the infinite union, for all natural numbers N, of
a hierarchy of classes of languages (each definable in several ways) in
which the first few steps are these:



N = 0 finite languages
N = 1 regular (finite-state) languages
N = 2 context-free languages
N = 3 tree adjoining languages
N = 4 ...



There has been much relevant mathematical work on these matters
between 1984 and the present by people like Gerald Gazdar, Henk
Harkema, Aravind Joshi, Greg Kobele, Jens Michaelis, Carl Pollard,
Kelly Roach, James Rogers, Edward Stabler, K. Vijay-Shanker, and
David Weir (it is easily findable; I will not try to give even a brief
bibliography here.) If Stabler has accurately captured the intent of the
hints in the "minimalist program" about Merge and feature-checking,
then minimalism embraces an enormous proper superset of the
context-free languages. (I say "if" because Chomsky declines to refer
to any of Stabler's work, so we don't know whether the formalization is
acceptable as a precise reconstruction of the minimalist program as he
conceives of it.)



Clark was trying to get Chomsky's reaction to recent results (see e.g.
Clark 2010) exhibiting efficient algorithms for learning various
subclasses of the MCFGs, including some fairly large classes going
well beyond CFGs.



Chomsky interrupted the question and began to talk about CFGs. But
he misspoke, and talked about having proved in 1959 that CFGs are
equivalent to linear bounded automata (they aren't; LBAs are
equivalent to context-sensitive grammars). Even if CFGs had been
equivalent to LBAs, and even if Chomsky had been responsible for
results on LBAs in 1959 (he wasn't, it was Kuroda five years later),
CFGs had nothing to do with the observation Clark was trying to make
about MCFGs. And Chomsky had in any case never proved any
theorems about learnability, which was what Clark was trying to ask
about. Clark's question not only was never answered, it was not even
heard, hence of course not understood.



6. Languages evolving



After Clark's question, there were only a few more. I was lucky enough
to be allocated time to ask two brief questions before the session
ended. Chomsky had condemned language evolution work wholesale
("a burgeoning literature, most of which in my view is total nonsense":
UCL video, 27:08), and I asked him to speak more directly about
Simon Kirby's research on iterated learning of initially randomly
structured finite languages, which he has shown leads to the rapid
evolution of morphological regularity.



Chomsky's answer was that it is not at all interesting if successive
generations of learners regularize the language they are trying to
learn: the regularity emerges only because human intelligence and
linguistic competence is utilized in the task, and if you gave the same
task to computers the same evolution would not happen.



Kirby's group has in fact addressed both those points, and both claims
appear to be false. It seems to be the cognitive bottleneck of memory
limitation that forces the emergence of regularity (decrease in
Kolmogorov complexity) in the language over learning generations, not
human linguistic capacity or intelligence (note the remark of Kirby,
Cornish, & Smith 2008: 10685, that "if participants were merely
stamping their own linguistic knowledge onto the data that they were
seeing, there would be no reason we would find rampant structured
underspecification in the first experiment and a system of
morphological concatenation in the second"). And the effect of weak
learning bias being amplified by cultural transmission through iterated
learning does indeed turn up when the learner is simulated on a
computer (see e.g. Kirby, Dowman, and Griffiths 2007).



There is an opportunity for substantive discussion here. And since
both Chomsky and Kirby are invited speakers at the upcoming
EvoLang conference in Kyoto (http://kyoto.evolang.org/), there will be a
forum where it could happen. I hope it will. But maybe I'm too
optimistic: I see the current integration of computationally-assisted
cognitive science with careful syntactic description and theorizing as
precisely what should inspire confidence that the language sciences in
the 21st century has a bright future rather than spelling doom to
linguistics.



7. Genetic fixity



The other topic I was able to ask about was the scientific plausibility of
a view that has a remarkable genetic quirk arising between 50,000 and
200,000 years ago, giving a single developing hominid species an
unprecedented innate UG that permits articulate linguistic capacities,
and then remaining absolutely fixed in all of its details until the present.



A very few linguists (they include James McCawley, Geoffrey
Sampson, and Philip Lieberman) have pointed out this prediction of
genetically determined variation in UG between widely separated
human groups. Lieberman notes that dramatic evolutionary
developments like disappearance of lactose intolerance or radical
alteration in the ability to survive in high-altitude low-oxygen
environments can take place in under 3000 years; yet (as Chomsky
stresses) the evidence that any human being can learn any human
languages is strong, suggesting that UG shows no genetic variation at
all.



Why would UG remain so astonishingly resistant to minor mutations for
so many tens of thousands of years? There is no selection pressure
that would make it disadvantageous for Australian aborigines to have
different innate constraints on movement or thematic role assignment
from European or African populations; yet not a hint of any such
genetic diversity in innate linguistic capacities has ever been identified,
at least in grammar. Why not?



Chomsky's response is basically that it just happened. He robustly
insists that this kind of thing happens all the time in genetics: all sorts
of developments in evolution occur once and then remain absolutely
fixed, like the architecture of our visual perception mechanism. Human
beings, he told me solemnly, are not going to develop an insect visual
system over the coming 50,000 years.



This was his final point before his schedule required him to leave, and I
had to agree with him (so let's not have any loose talk about kneejerk
disagreement, OK?) --- we're not going to develop insect eyes. But I
couldn't help thinking that this hardly answered the question. There are
parts of our genome that remain identical for hundreds of millions of
years, like HOX genes; but generally they cause catastrophic effects
on the organism if incorrectly expressed. Even with the visual system,
arbitrary changes could put an organism in real trouble. For widely
separated populations of humans to have different constraints on
remnant movement wouldn't do any damage at all, and it would offer
dramatic support for the view that there is a genetically inherited syntax
module (though the "U" of UG would now not be so appropriate).



So it was just as with the rocks-and-kittens POS argument: I agree with
the starting observations, as everyone must; but the broader
conclusions that Chomsky defends, and more generally his extremely
negative attitude to computer simulation work, human-subject
experimentation, evolutionary investigations, and data-intensive
research don't seem to follow.



I am not pessimistic enough to believe that contemporary experimental
research in the cognitive and linguistic sciences --- Bayesian and
connectionist work included --- will prove to be some kind of toxic
threat to our discipline. I think it represents an encouragingly lively and
stimulating contribution. I think we have a responsibility as academics
to acknowledge such work and do our best to appreciate its methods
and results. It won't do anything clarify our understanding of language
if we simply condemn it all out of hand.



Geoff Pullum
University of Edinburgh



REFERENCES



Berwick, Robert; Paul Pietroski; Yankama; and Noam Chomsky (2011).
[BPYC-2011] Poverty of the stimulus revisited. Cognitive Science 35:
1207–1242.



Chomsky, Noam (2000). The Architecture of Language. New Delhi:
Oxford University Press.



Chomsky, Noam (2011). Language and other cognitive systems: What
is special about language? Language Learning and Development 7
(4): 263-278. http://dx.doi.org/10.1080/15475441.2011.584041



Clark, Alexander (2010). Efficient, correct, unsupervised learning of
context-sensitive languages. Proceedings of the Fourteenth
Conference on Computational Natural Language Learning, 28-37.
Uppsala, Sweden: Association for Computational Linguistics.
http://www.cs.rhul.ac.uk/home/alexc/papers/conll2010.pdf



Clark, Alexander, and Remi Eyraud (2007). Polynomial time
identification in the limit of substitutable context-free languages.
Journal of Machine Learning Research, 8, 1725–1745.



Kirby, Simon; Michael Dowman; and Thomas Griffiths (2007).
Innateness and culture in the evolution of language. Proceedings of
the National Academy of Sciences, 104 (12): 5241-5245.



Kirby, Simon; Hannah Cornish; and Kenny Smith (2008). Cumulative
cultural evolution in the laboratory: An experimental approach to the
origins of structure in human language. Proceedings of the National
Academy of Sciences, 105 (31): 10681-10686.



Lightfoot, David (1998). Promises, promises: general learning
algorithms. Mind and Language 13: 582-587.



Scholz, Barbara C. and Geoffrey K. Pullum (2002) Searching for arguments
to support linguistic nativism. The Linguistic Review 19, 185-223.



Seki, Matsumura and Kasami Fujii (1991). On multiple context-free
grammars. Theoretical Computer Science 88: 191-229.



Smith, Neilson Voyne (1999). Chomsky: Ideas and Ideals. Cambridge:
Cambridge University Press.



Stabler, Edward (1997). Derivational minimalism. Christian Retore,
Logical Aspects of Computational Linguistics (Lecture Notes in Artificial
Intelligence, 1328), 68-95. Berlin: Springer Verlag.



Stemmer, Brigitte (1999). An on-line interview with Noam Chomsky:
On the nature of pragmatics and related issues. Brain and Language
68 (3): 393-401.
Date Posted: 19-Nov-2011
Linguistic Field(s): Computational Linguistics
Cognitive Science
Discipline of Linguistics
LL Issue: 22.4631
Posted: 19-Nov-2011

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