  In order to build efficient deep recurrent neural architectures, it is
essential to analyze the complexityof long distance dependencies (LDDs) of the
dataset being modeled. In this paper, we presentdetailed analysis of the
dependency decay curve exhibited by various datasets. The datasets sampledfrom
a similar process (e.g. natural language, sequential MNIST, Strictlyk-Piecewise
languages,etc) display variations in the properties of the dependency decay
curve. Our analysis reveal thefactors resulting in these variations; such as
(i) number of unique symbols in a dataset, (ii) size ofthe dataset, (iii)
number of interacting symbols within a given LDD, and (iv) the distance
betweenthe interacting symbols. We test these factors by generating synthesized
datasets of the Strictlyk-Piecewise languages. Another advantage of these
synthesized datasets is that they enable targetedtesting of deep recurrent
neural architectures in terms of their ability to model LDDs with
differentcharacteristics. We also demonstrate that analysing dependency decay
curves can inform the selectionof optimal hyper-parameters for SOTA deep
recurrent neural architectures. This analysis can directlycontribute to the
development of more accurate and efficient sequential models.
