Learning with Interpretable Structure from Gated RNN

Understand

The interpretability of deep learning models has raised extended attention these years.

• It will be beneficial if we can learn an interpretable structure from deep learning models.
• In this paper, we focus on Recurrent Neural Networks~(RNNs) especially gated RNNs whose inner mechanism is still not clearly understood.
• We find that Finite State Automaton~(FSA) that processes sequential data has more interpretable inner mechanism according to the definition of interpretability and can be learned from RNNs as the interpretable structure.