  In order to quantize the gate parameters of the LSTM (Long Short-Term Memory)
neural network model with almost no recognition performance degraded, a new
quantization method named Quantization Loss Re-Learn Method is proposed in this
paper. The method does lossy quantization on gate parameters during training
iterations, and the weight parameters learn to offset the loss of gate
parameters quantization by adjusting the gradient in back propagation during
weight parameters optimization. We proved the effectiveness of this method
through theoretical derivation and experiments. The gate parameters had been
quantized to 0, 0.5, 1 three values, and on the Named Entity Recognition
dataset, the F1 score of the model with the new quantization method on gate
parameters decreased by only 0.7% compared to the baseline model.
