A Method to Judge the Style of Classical Poetry Based on Pre-trained Model
Ziyao Wang, Jiandong Zhang, Jun Ma

TL;DR
This paper develops a pre-trained BART-based model trained on a comprehensive Chinese classical poetry dataset to objectively analyze and identify poetic styles, improving accuracy over subjective traditional methods.
Contribution
It introduces a novel computational approach using deep learning for style judgment in classical Chinese poetry, with a new dataset and model training methodology.
Findings
Model's judgments align with traditional critics' conclusions.
Verifies some avant-garde opinions of Mr. Qian Zhongshu.
Enhances style recognition accuracy for Tang and Song dynasty poetry.
Abstract
One of the important topics in the research field of Chinese classical poetry is to analyze the poetic style. By examining the relevant works of previous dynasties, researchers judge a poetic style mostly by their subjective feelings, and refer to the previous evaluations that have become a certain conclusion. Although this judgment method is often effective, there may be some errors. This paper builds the most perfect data set of Chinese classical poetry at present, trains a BART-poem pre -trained model on this data set, and puts forward a generally applicable poetry style judgment method based on this BART-poem model, innovatively introduces in-depth learning into the field of computational stylistics, and provides a new research method for the study of classical poetry. This paper attempts to use this method to solve the problem of poetry style identification in the Tang and Song…
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Taxonomy
TopicsAsian Culture and Media Studies · Computational and Text Analysis Methods · Advanced Computing and Algorithms
