ProtoryNet - Interpretable Text Classification Via Prototype Trajectories

Understand

We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on 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 sentence to the corresponding active prototype.
• The RNN backbone then captures the temporal pattern of the prototypes, which we refer to as prototype trajectories.
• Prototype trajectories enable intuitive and fine-grained interpretation of the reasoning process of the RNN model, in resemblance to how humans analyze texts.