关键词: 循环神经网络（RNN）, 长短时记忆网络（LSTM）, 门限循环单元（GRU）, 口语理解（SLU）, 改进循环神经网络（M-RNN）

Abstract: The improvement of Spoken Language Understanding（SLU） plays an important role in spoken dialogue system. Recurrent neural network and its variants are used to improve performance of SLU. The modified recurrent neural network algorithm is proposed to enhance the SLU, which adds the memory of longer historical information and has fewer parameters. This method can efficiently obtain feature information that not only improves the precision and [F1] but also cut down experimental period. The experimental results on the ATIS corpus verify the effectiveness and reliability of the proposed algorithm.

Key words: Recurrent Neural Network（RNN）, Long Short-Term Memory（LSTM）, Gated Recurrent Unit（GRU）, Spoken Language Understanding（SLU）, Modified Recurrent Neural Networks（M-RNN）