  Intent detection is a text classification task whose aim is to recognize and
label the semantics behind a users query. It plays a critical role in various
business applications. The output of the intent detection module strongly
conditions the behavior of the whole system. This sequence analysis task is
mainly tackled using deep learning techniques. Despite the widespread use of
these techniques, the internal mechanisms used by networks to solve the problem
are poorly understood. Recent lines of work have analyzed the computational
mechanisms learned by RNNs from a dynamical systems perspective. In this work,
we investigate how different RNN architectures solve the SNIPS intent detection
problem. Sentences injected into trained networks can be interpreted as
trajectories traversing a hidden state space. This space is constrained to a
low-dimensional manifold whose dimensionality is related to the embedding and
hidden layer sizes. To generate predictions, RNN steers the trajectories
towards concrete regions, spatially aligned with the output layer matrix rows
directions. Underlying the system dynamics, an unexpected fixed point topology
has been identified with a limited number of attractors. Our results provide
new insights into the inner workings of networks that solve the intent
detection task.
