Generating Major Types of Chinese Classical Poetry in a Uniformed Framework

Jinyi Hu, Maosong Sun


Abstract
Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form- stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University.
Anthology ID:
2020.lrec-1.573
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4658–4663
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.573
DOI:
Bibkey:
Cite (ACL):
Jinyi Hu and Maosong Sun. 2020. Generating Major Types of Chinese Classical Poetry in a Uniformed Framework. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4658–4663, Marseille, France. European Language Resources Association.
Cite (Informal):
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework (Hu & Sun, LREC 2020)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/2020.lrec-1.573.pdf