  Common wisdom has it that the bias of stochastic grammars in favor of shorter
derivations of a sentence is harmful and should be redressed. We show that the
common wisdom is wrong for stochastic grammars that use elementary trees
instead of context-free rules, such as Stochastic Tree-Substitution Grammars
used by Data-Oriented Parsing models. For such grammars a non-probabilistic
metric based on the shortest derivation outperforms a probabilistic metric on
the ATIS and OVIS corpora, while it obtains very competitive results on the
Wall Street Journal corpus. This paper also contains the first published
experiments with DOP on the Wall Street Journal.
