关键词: 槽填充, 意图识别, 联合模型, 深度学习

Abstract: Most of the previous studies explored Spoken Language Understanding（SLU） framework for building separate model for each task, such as slot filling or intent detection, ignoring the mutual promotion of this tasks. A BLSTM-CNN-CRF architecture for joint modeling of slot filling and intent detection is proposed based on the advantages of deep learning. Each word-level label is greedily determined by the hidden representation from the Bi-directional Long Short-Term Memory（BLSTM） network in each step. The semantic features of the whole utterance are automatically extracted through Convolutional Neural Network（CNN）. Finally, the output vectors of them are fed to the Conditional Random Fields（CRF） layer to jointly decode the best label sequence. The joint model achieves competitive performance on the benchmark Airline Travel Information System（ATIS） task without any artificial features.

Key words: slot filling, intent detection, joint model, deep learning