| |
Abstract:
Traditional theories of child language acquisition center
around the existence of innate, domain-specific parameters which
are specifically tuned for learning a particular class of
languages. More recent proposals suggest that language acquisition
is assisted by the evolution of languages towards forms that are
easily learnable. In this paper, we evolve combinatorial languages
which can be learned by a recurrent neural network quickly and from
relatively few examples. Additionally, we evolve languages for
generalization in different "worlds", and for generalization from
specific examples. We find that languages can be evolved to
facilitate different forms of impressive generalization for a
general purpose learner. The results provide empirical support for
the theory that the language itself, as well as the language
environment of a learner, plays a substantial role in learning:
that there is far more to language acquisition than the language
acquisition device.
|
