  Despite the widespread application of recurrent neural networks (RNNs) across
a variety of tasks, a unified understanding of how RNNs solve these tasks
remains elusive. In particular, it is unclear what dynamical patterns arise in
trained RNNs, and how those patterns depend on the training dataset or task.
This work addresses these questions in the context of a specific natural
language processing task: text classification. Using tools from dynamical
systems analysis, we study recurrent networks trained on a battery of both
natural and synthetic text classification tasks. We find the dynamics of these
trained RNNs to be both interpretable and low-dimensional. Specifically, across
architectures and datasets, RNNs accumulate evidence for each class as they
process the text, using a low-dimensional attractor manifold as the underlying
mechanism. Moreover, the dimensionality and geometry of the attractor manifold
are determined by the structure of the training dataset; in particular, we
describe how simple word-count statistics computed on the training dataset can
be used to predict these properties. Our observations span multiple
architectures and datasets, reflecting a common mechanism RNNs employ to
perform text classification. To the degree that integration of evidence towards
a decision is a common computational primitive, this work lays the foundation
for using dynamical systems techniques to study the inner workings of RNNs.
