Before revision:  Bi-LSTM relieves the vanishing gradient problem, and attentional mechanisms allows learning and interpretation of feature importance  .
After revision:  Bi-LSTM relieves the vanishing gradient problem, and attentional mechanisms allows learning and interpretation of feature importance , and complex-valued networks inspired quantum physics to facilitate a certain level of transparency in modeling the decision process .