关键词: 口语理解, 循环神经网络, 长短时记忆网络, 神经图灵机

Abstract: Recurrent Neural Network（RNN） has increasingly shown its advantages in the Spoken Language Understanding（SLU） task. However, because of the problem of gradient disappearance and gradient explosion, the storage capacity of simple recurrent neural network is limited. A RNN that uses external memory is proposed to improve memory. Experiments are carried out on the ATIS data set and compared with other publicly reported models. The results show that, in oral comprehension tasks, the RNN introduced external memory has significantly improved accuracy, recall rate and F1-score, which is superior to traditional recurrent neural network and its variant structure.

Key words: Spoken Language Understanding（SLU）, Recurrent Neural Network（RNN）, Long Short Term Memory（LSTM） network, neural turing machine