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Paper in preparation. Some of the material will be presented at GALA
1997: Language Acquisition: Knowledge Representation and Processing,
University of Edinburgh.
Language Acquisition: Learning and Applying Probabilistic
Constraints
Mark S. Seidenberg, Joseph Allen & Morten H. Christiansen
Program in Neural, Informational and Behavioral Sciences
University of Southern California
University Park MC-2520
Los Angeles, CA 90089-2520
Abstract
We will describe a new framework for thinking about language
acquisition that is emerging from renewed interest in statistical and
probabilistic aspects of language, insights from connectionism, and
recent behavioral studies of the learning capacities of
infants. Standard approaches to child language equate knowledge of
language with a competence grammar. The child's task is grammar
identification and standard poverty of the stimulus arguments suggest
that this task is intractable unless there is significant innate
grammatical knowledge. The newer approach has three main
components. First, there are analyses of the actual utterances to
which children are exposed suggesting that they contain a rich set of
probabilistic cues to different aspects of linguistic structure. This
kind of statistical information has been excluded from competence
grammar ever since "colorless green ideas sleep furiously." Second,
there is theoretical work on how such cues can be extracted,
represented, and combined. Much of this research draws on
connectionist concepts of knowledge representation, learning, and
processing. The constraint satisfaction process that such networks
implement shows how probabilistic cues can be combined in powerful
ways. Even though particular cues may not be highly reliable in
isolation, combinations of such cues yield non-linear increases in
their informativeness. Such networks provide a computationally
explicit interpretation of the concept of "bootstrapping." Finally,
behavioral studies of infants are beginning to show that they rapidly
and effortlessly pick up this kind of statistical information from a
very young age (e.g., Saffran, Newport & Aslin, in press). Recent
applications of this framework include the Christiansen, Allen &
Seidenberg's (in press) work on the word segmentation problem and
Allen's (1996) work on the acquisition of verbs and their argument
structures.
This framework raises a number of important issues. First, it calls
into question many deeply-held assumptions about the nature of
language acquisition. In particular, many of the standard poverty of
the stimulus arguments no longer apply. For example, the approach
explains how children could converge on essentially the same knowledge
of language despite variability in experience; and, there is no
negative evidence problem because acquisition is driven by analyses of
statistical regularities in the input, not feedback about the
grammaticality of utterances. Second, the approach provides closer
ties between acquisition and skilled performance. We ask how the child
acquires the capacity to comprehend and produce utterances and draw
upon theories of adult performance (e.g., MacDonald, Pearlmutter &
Seidenberg, 1994) in framing questions about language
learning. Finally, there are questions about the adequacy of the
framework: can it explain a broad range of facts about the structure
of language and how children acquire it; is it compatible with other
things that are known about the brain bases of language and about the
relationship between language and other aspects of cognition?
Click to request a copy of this paper.
