从整体到局部优化的文本风格迁移模型OA北大核心CSTPCD

A global-local based style transfer(G-LST)framework is proposed,optimizing from a glob-al to a local level.Firstly,extensive source-side data is used for iterative optimization to automatically construct high-quality pseudo-parallel data,and through joint training to improve the model's seman-tic perception of the global style.Subsequently,the model enhances the local style representation by modifying the style at the word level with commonsense knowledge.This method considers both global and local styles simultaneously,thereby improving the accuracy of style transfer.Experimental results on the GYAFC dataset show that compared with the state-of-the-art text style transfer model,the G-LST model's style transfer accuracy on data in the E&M and F&R fields has increased by 2.70%and 4.47%respectively.The comprehensive metrics for content preservation and style accuracy have improved by 1.18%and 1.95%respectively.