Text Score Analysis under the IPE Environment Based on Improved Transformer

In order to improve the accuracy of ideological and political education (IPE) text scoring, an improved short-text similarity calculation model based on transformer is proposed. This model takes the DSSM model as the basic framework and uses the Bert model to realize text representation and solve polysemy problem. The transformer encoding component is used to extract the characteristics of the text and obtain the internal information of the text. With the help of the encoding component, the two texts can interact with information on multiple levels. Finally, the semantic similarity between two texts is calculated by concatenation vector inference.