We propose a novel interpretable recurrent neural network (RNN) model, called
ProtoryNet, in which we introduce a new concept of prototype trajectories.
Motivated by the prototype theory in modern linguistics, ProtoryNet makes a
prediction by finding the most similar prototype for each sentence in a text
sequence and feeding an RNN backbone with the proximity of each of the
sentences to the prototypes. The RNN backbone then captures the temporal
pattern of the prototypes, to which we refer as prototype trajectories. The
prototype trajectories enable intuitive, fine-grained interpretation of how the
model reached to the final prediction, resembling the process of how humans
analyze paragraphs. Experiments conducted on multiple public data sets reveal
that the proposed method not only is more interpretable but also is more
accurate than the current state-of-the-art prototype-based method. Furthermore,
we report a survey result indicating that human users find ProtoryNet more
intuitive and easier to understand, compared to the other prototype-based
methods