Dekai Wu
ABSTRACT
In this paper, we describe a probabilistic framework for unification-based grammars that facilitates integrating syntactic and semantic constraints and preferences. We share many of the concerns found in recent work on massively-parallel language interpretation models, although the proposal reflects our belief in the value of a higher-level account that is not stated in terms of distributed computation. We also feel that inadequate learning theories severely limit existing massively-parallel language interpretation models. A learning theory is not only interesting in its own right, but must underlie any quantitative account of language interpretation, because the complexity of interaction between constraints and preferences makes ad hoc trial-and-error strategies for picking numbers infeasible, particularly for semantics in realistically-sized domains.
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