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 fi'om using as much syutactic 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 fi'om a tree-annotated corpus improves on a tree model derived from an unarmorated corpus, and that a tree-based stochastic model with a hand- crafted grammar outperforms both.

Citations