Predicting the Chinese Poetry Prosodic Based on a Developed BERT Model

The prosody is an integration of the formal beauty and the content beauty of Chinese classical poetry. In this study, a prosodic structure prediction method based on a pretrained language representation model was proposed by us. Based on the pretrained language representation model, a separate output layer was set for each prosody level, with character as the modeling unit. Then the model was fine-tuned with the labeled prosody data. To achieve the simultaneous prediction of different prosodic levels in the input text, a word segmentation task was additionally introduced and the multitask learning method was used to model the relationship between the multilevel prosody and lexicon words. The experimental results verify that adding the word segmentation task can further improve model performance and demonstrate that the proposed method considerably reduces the demand for training data while maintaining an excellent prediction performance.