Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention from model spo Watch Video
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Description: We present a generative neural network model for slot filling based on a sequence- to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spo-ken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an atten-tional Seq2Seq model. Experimental resu