GPT-based Generation for Classical Chinese Poetry

Abstract

We present a simple yet effective method for generating high quality classical Chinese poetry with Generative Pre-trained Language Model (GPT). The method adopts a simple GPT model, without using any human crafted rules or features, or designing any additional neural components. While the proposed model learns to generate various forms of classical Chinese poems, including Jueju, Lüshi, various Cipai and Couples, the generated poems are of very high quality. We also propose and implement a method to fine-tune the model to generate acrostic poetry. To the best of our knowledge, this is the first to employ GPT in developing a poetry generation system. We have released an online mini demonstration program on Wechat to show the generation capability of the proposed method for classical Chinese poetry.

References in corpus (2)

• The Curious Case of Neural Text Degeneration
• Chinese Poetry Generation with Planning based Neural Network

Cited by in corpus (6)

• Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training
• An Approach to Improve Robustness of NLP Systems against ASR Errors
• DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
• Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game
• Generating Major Types of Chinese Classical Poetry in a Uniformed Framework
• SongNet: Rigid Formats Controlled Text Generation