One paper entitled Towards Interpreting Recurrent Neural Network through Probabilistic Abstraction is accepted by ASE 2020.
This work bridges the gap between black-box recurrent neural networks (RNN) and model-based analysis approaches by automatically extracting more tracable, reasonable and transparent models, i.e., probabilistic finite automata, from RNN.