Research on Semantic Verification Method of Land and Air Calls Based on Transfer Learning
In order to solve the problem of determining whether the pilot's recitation of instructions is consistent with the in-structions given by the controller,a semantic verification model for land-air calls is constructed,it is Ernie-Gram-BiGRU-Atten-tion.First,Ernie-Gram module is used to encode the text-to-message information.BiGRU module is used to further extract the fea-tures from the encoded text-to-message information to obtain the compressed sentence vector.Since the keyword information is cru-cial in land-air calls,the attention mechanism is used to realize the information extraction of keyword features,and the fully con-nected layer is used to fuse the attention vector with the sentence vector.Finally,the fused vectors are spliced with the categorized word vectors output from the Ernie-Gram module and sent to the fully-connected layer for semantic verification of text pairs.The constructed model achieves 97.1%accuracy on the land-air call text pair dataset,which is a 2%improvement in accuracy compared to the Ernie-Gram benchmark model,and an improvement in precision,recall,F1 value,and accuracy compared to other Bert-based improved models.
air traffic safetytransfer learningland-air callsErnie-Gramattention mechanisms
万方数据
维普
