Knowledge-Guided Transformer for Joint Theme and Emotion Classification of Chinese Classical Poetry

The classifications of the theme and emotion are essential for understanding and organizing Chinese classical poetry. Existing works often overlook the rich semantic knowledge derived from poem annotations, which contain crucial insights into themes and emotions and are instrumental in semantic understanding. Additionally, the complex interdependence and diversity of themes and emotions within poems are frequently disregarded. Hence, this paper introduces a Poetry Knowledge-augmented Joint Model (Poka) specifically designed for the multi-label classification of themes and emotions in Chinese classical poetry. Specifically, we first employ an automated approach to construct two semantic knowledge graphs for theme and emotion. These graphs facilitate a deeper understanding of the poems by bridging the semantic gap between the obscure ancient words and their modern Chinese counterparts. Representations related to themes and emotions are then acquired through a knowledge-guided mask-transformer. Moreover, Poka leverages the inherent correlations between themes and emotions by adopting a joint classification strategy with shared training parameters. Extensive experiments demonstrate that our model achieves state-of-the-art performance on both theme and emotion classifications, especially on tail labels.