Enhancing Emotional and Cultural Retention in Ancient Chinese Poetry Translation Using BERT

Abstract

This research investigates the use of the BERT (Bidirectional Encoder Representations from Transformers) model to enhance the translation of ancient Chinese poetry into English, with particular focus on overcoming the unique challenges posed by this literary genre. Ancient Chinese poetry is renowned for its intricate rhythm, tonal variations, and dense symbolism, all deeply embedded within cultural and historical contexts. These features create significant difficulties in translation, as maintaining the original’s lyrical quality, emotional depth, and rich cultural references often proves elusive using conventional methods. Through a meticulous process of curating and annotating a dataset comprising ten masterworks of the Tang and Song dynasties, this study explores whether BERT’s advanced contextual understanding and bidirectional encoding can more effectively convey the nuanced emotional and cultural content embedded in the source texts. The results demonstrate that BERT substantially improves both affective resonance and the preservation of cultural imagery in English translations, achieving a higher level of fluency and authenticity. This work not only advances the capabilities of machine translation for complex literary forms but also underscores the potential of cutting-edge AI to foster deeper cross-cultural understanding and greater global appreciation of China’s rich poetic heritage.