其中AP-CNN和AP-biLSTM是对前两种模型的改进,即引入了attention机制。主要参考论文《Attentive Pooling Networks》 Co-attention机制是近年来新出现的处理序列信息匹配的机制。 本文末尾给出了模型代码和实验结果。 经典问答系统模型 问题文本和答案文本分别喂入两个同样的CNN或LSTM网络。若编码层采用卷积网络CNN处理,称...
AP-BILSTM-maxpooling/polymerization.py/ Jump to 98 lines (84 sloc)6.02 KB RawBlame importtensorflowastf frombilstmimportBILSTM fromutilsimportcal_attention,max_pooling,ortho_weight,uniform_weight,feature2cos_sim,cal_loss_and_acc classLSTM(object): ...
Sign inSign up person-lee/AP-BILSTM-maxpooling Watch1 Star2 Fork2 Code Issues1 Pull requests Actions Projects Security Insights More master AP-BILSTM-maxpooling/execute.py/ Jump to 158 lines (135 sloc)7.95 KB RawBlame # coding=utf-8 ...
In addition, the superiority of XLNet in NER tasks is demonstrated. We evaluate our model on the CoNLL-2003 English dataset and WNUT-2017 and show that the XLNet-BiLSTM-CRF obtains state-of-the-art results. 展开 关键词: Natural language process Named entity recognition XLNet Bi-directional ...