Bert model-based intention recognition and slot value filling combined prediction method
A prediction method and technology of intent, applied in character and pattern recognition, neural learning methods, biological neural network models, etc., can solve the problems affecting the quality of the final dialogue system, low accuracy of slot value filling, etc., to reduce task error prediction, Avoid the effect of superimposed error rate and high prediction accuracy
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[0053] Dataset and model input:
[0054] The datasets used are the dataset from the Snips personal voice assistant and the Atis dataset from the flight reservation system. Among them, the training set, test set and verification set data in the Snips dataset are 13084, 700 and 700 sentences respectively, and the training set, test set and verification set data in the Atis dataset are 4478, 500 and 893 sentences respectively.
[0055] The input representation of the model is a word embedding (E w ), position embedding (E p ) and segment embedding (E s ) cascade. Use the WordPiece model to process the input sentence, insert the [CLS] tag before the sentence as a classification token, insert [SEP] at the end of the sentence as the ending token, and generate a word embedding (E w =(E CLS ,E w1 ......,E wT ,E Seq )). Since we are targeting intent classification and slot value identification for a single sentence, the segment embeddings for each sentence are all 0s. The pos...
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