User Intent Classification Based on ALBERT,Lattice IndyLSTM and Attention
A new user intent classification model is proposed for the task of intent recognition in conversational AI systems.This model combines the ALBERT pre-training model,Lattice IndyLSTM(Lattice Independently Recurrent Long Short Term Mem-ory)in a lattice form,and word-level attention fusion mechanism.By constructing a lattice composed of character embeddings and word embeddings and inputting it into the IndyLSTM network,this model can handle the challenges in traditional intent classifica-tion tasks,such as the inability to simultaneously utilize character and word information,the gradient explosion or vanishing in RNNs,and LSTM model overfitting.Furthermore,by utilizing the word-level attention mechanism that enhances the contribution of domain-specific vocabulary in user input sentences,the accuracy of intent recognition is greatly improved.Experimental results demonstrate that the proposed user intent classification model effectively enhances precision,recall,F1-score,and other perfor-mance metrics.
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