Long text semantic matching model based on BERT and dense composite network
In the semantic matching of long texts,it is challenging to capture the before-and-after connections and topic information,which often results in poor semantic matching.This paper proposes a long text semantic matching method based on BERT and dense composite network.Through the dense connection of BERT embedding and composite network,the accuracy of long semantic matching is significantly improved.First,the sentence pair is input into the BERT pre-training model,and accurate word vector representation is obtained through iterative feedback,and then high-quality sentence pair semantic information is obtained.Secondly,a dense composite network is designed.Bi-LSTM first obtains the global semantic information of sentence pairs,and then TextCNN extracts and integrates local semantic information to obtain the key features of each sentence and the correspondence between sentence pairs,and the BERT Fusion with the hidden output of Bi-LSTM and the pooled output of TextCNN.Finally,summarizing the association state between networks during the training process can effectively prevent network degradation and enhance the model's judgment ability.The experimental results show that on the community question answering(CQA)long text dataset,the method in this paper has a significant effect,with an average improvement of 45％.
deep learninglong text semantic matchingBERTdense composite networkBi-LSTMTextCNN
万方数据
维普
