Language acquisition in the absence of explicit negative evidence: can simple recurrent networks obviate the need for domain-specific learning devices?
Gf. Marcus, Language acquisition in the absence of explicit negative evidence: can simple recurrent networks obviate the need for domain-specific learning devices?, COGNITION, 73(3), 1999, pp. 293-296
Rohde & Plaut, 1999 argue that their work with Elman' s simple recurrent ne
twork (henceforth, SRN) "suggests that learning the structure of natural la
nguage may be possible despite a lack of explicit negative feedback.,, in t
he absence of detailed innate language-acquisition mechanisms". They furthe
r argue that "a key factor in overcoming the 'logical problem' of language
acquisition (Baker&McCarthy, 1981) is the use of implicit negative evidence
." (Implicit negative evidence is information about something that does not
appear when it was predicted to appear.)
R&P are surely correct that some versions of the simple recurrent network d
o not rely on negative evidence and that such networks are able in some cas
es to utilize implicit negative evidence.(1) But R&P do not show that these
models avoid the kinds of errors that children make, do not show that thes
e models derive the same generalizations as children do, and do not show th
at these models use indirect negative evidence in ways that would obviate t
he need for innate, domain-specific learning devices. All that they offer i
s a simulation of a tiny fragment of a simplified version of English; they
do not fit the model's data against any data derived from children. Their s
ystem does not provide any sort of syntactic or semantic representation of
the sentences that it is exposed to, and it does not make a principled dist
inction between infrequent and ungrammatical sentences. This is not enough
to establish the adequacy of the model, and more careful inspection reveals
a serious, principled limitation that stems directly from its treatment of
implicit negative evidence.