  A sharp tension exists about the nature of human language between two
opposite parties: those who believe that statistical surface distributions, in
particular using measures like surprisal, provide a better understanding of
language processing, vs. those who believe that discrete hierarchical
structures implementing linguistic information such as syntactic ones are a
better tool. In this paper, we show that this dichotomy is a false one. Relying
on the fact that statistical measures can be defined on the basis of either
structural or non-structural models, we provide empirical evidence that only
models of surprisal that reflect syntactic structure are able to account for
language regularities.
