Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
Lin Zhao, Zhe Feng · 2018
We present a generative neural network model for slot filling based on a sequenceto-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken 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 attentional Seq2Seq model.Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem.Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.