Abstract
Classical Chinese poetry, as the cultural heritage of human beings, is very popular in Chinese community all over the world. Nearly every person in these regions can recite several poems to artistically express his or her emotion. However, due to the huge difference between the archaic and modern Chinese language, now, it is hard for people to understand these poems. Considering the Neural machine translation (NMT) has made great progress since the self-attention technology called Transformer was adopted, we followed the paradigm of NMT to translate the classical Chinese poems into modern Chinese texts in this study. First, we collect many ancient poems and their explanations as a parallel corpus. Then, a NMT system is trained with this corpus. Finally, we compared this system with Generative Pre-Training (GPT) and analysis their results in detail.
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Acknowledgments
This study is supported by the fundamental research of Leshan Normal University (grant no. 801 205190117) and Natural Science Foundation of China (no. 61373056). We will thank the anonymous reviewers for their valuable comments.
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Jin, P., Wang, H., Ma, L., Wang, B., Zhu, S. (2022). Translating Classical Chinese Poetry into Modern Chinese with Transformer. In: Dong, M., Gu, Y., Hong, JF. (eds) Chinese Lexical Semantics. CLSW 2021. Lecture Notes in Computer Science(), vol 13249. Springer, Cham. https://doi.org/10.1007/978-3-031-06703-7_37
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DOI: https://doi.org/10.1007/978-3-031-06703-7_37
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