Exploiting a probabilistic hierarchical model for generation

Authors:
Srinivas Bangalore;Owen Rambow
Affiliations:
AT&T Labs - Research, Florham Park, NJ;AT&T Labs - Research, Florham Park, NJ
Venue:
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Year:
2000

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Abstract

Previous stochastic approaches to generation do not include a tree-based representation of syntax. While this may be adequate or even advantageous for some applications, other applications profit from using as much syntactic knowledge as is available, leaving to a stochastic model only those issues that are not determined by the grammar. We present initial results showing that a tree-based model derived from a tree-annotated corpus improves on a tree model derived from an unannotated corpus, and that a tree-based stochastic model with a hand-crafted grammar outperforms both.