  The choice of sentence encoder architecture reflects assumptions about how a
sentence's meaning is composed from its constituent words. We examine the
contribution of these architectures by holding them randomly initialised and
fixed, effectively treating them as as hand-crafted language priors, and
evaluating the resulting sentence encoders on downstream language tasks. We
find that even when encoders are presented with additional information that can
be used to solve tasks, the corresponding priors do not leverage this
information, except in an isolated case. We also find that apparently
uninformative priors are just as good as seemingly informative priors on almost
all tasks, indicating that learning is a necessary component to leverage
information provided by architecture choice.
