From sounds to words: Bayesian modeling of early language acquisition
Sharon Goldwater, Stanford University Department of Linguistics
The child learning language is faced with a difficult problem: given a
set of specific linguistic observations, the learner must infer some
abstract representation (a grammar) that generalizes correctly to
novel observations and productions. In this talk, I argue that
Bayesian computational models provide a principled way to examine the
kinds of representations, biases, and sources of information that lead
to successful learning. As an example, I discuss my work on modeling
word segmentation. I first present a computational study exploring the
effects of context on statistical word segmentation. In this study, a
model that assumes words are statistically independent (as in the
stimuli used in many human experiments) is compared to a model that
defines words as units that help to predict following words. I show
that the context-independent model undersegments the data, while the
contextual model yields much more accurate segmentations,
outperforming previous models on realistic corpus data. This
difference suggests the need to consider contextual effects in infant
word segmentation.
Simulations using corpus data provide insight into the kinds of
information that are useful for learning, but it is also important to
address the question of whether model predictions are consistent with
human learning patterns. In the second part of this talk, I present
results from a project designed to evaluate the predictions of various
word segmentation models. The human data is based on experiments
similar to those of Saffran et al. (1996), but several parameters of
the stimuli were varied between subjects to modify the difficulty of
the task. The Bayesian model described above correlates better with
human patterns of difficulty than any other model tested, suggesting
that this model does indeed capture important properties of human
segmentation.
If time allows, I will also discuss some work in progress in which I
am examining the acquisition of syntactic categories using similar
Bayesian modeling techniques.
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