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From Proceedings of the Eighteenth Annual Conference of the Cognitive
Science Society, 1996, p. 370-375. Mahwah, NJ: Lawrence Erlbaum
Associates.
Integrating Multiple Cues in Word Segmentation: A
Connectionist Model using Hints
Joe Allen and Morten H. Christiansen
Program in Neural, Informational and Behavioral Sciences
University of Southern California
University Park MC-2520
Los Angeles, CA 90089-2520
Abstract
Children appear to be sensitive to a variety of partially informative
``cues'' during language acquisition, but little attention has been
paid to how these cues may be integrated to aid learning. Borrowing
the notion of learning with ``hints'' from the engineering literature,
we employ neural networks to explore the notion that such cues may
serve as hints for each other. A first set of simulations shows that
when two equally complex, but related, functions are learned
simultaneously rather than individually, they can help bootstrap one
another (as hints), resulting in faster and more uniform learning. In
a second set of simulations we apply the same principles to the
problem of word segmentation, integrating two types of information
hypothesized to be relevant to this task. The integration of cues in a
single network leads to a sharing of resources that permits those cues
to serve as hints for each other. Our simulation results show that
such sharing of computational resources allows each of the tasks to
facilitate the learning (i.e., bootstrapping) of the other, even when
the cues are not sufficient on their own.
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