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Generation of Chinese classical poetry based on pre-trained model

arXiv 2211.02541 ↗First posted 4 Nov 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

In order to test whether artificial intelligence can create qualified classical poetry like humans, the author proposes a study of Chinese classical poetry generation based on a pre-trained model. This paper mainly tries to use BART and other pre training models, proposes FS2TEXT and RR2TEXT to generate metrical poetry text and even specific style poetry text, and solves the problem that the user's writing intention gradually reduces the relevance of the generated poetry text. In order to test the model's results, the authors selected ancient poets, by combining it with BART's poetic model work, developed a set of AI poetry Turing problems, it was reviewed by a group of poets and poetry writing researchers. There were more than 600 participants, and the final results showed that, high-level poetry lovers can't distinguish between AI activity and human activity, this indicates that the author's working methods are not significantly different from human activities. The model of poetry generation studied by the author generalizes works that cannot be distinguished from those of advanced scholars. The number of modern Chinese poets has reached 5 million. However, many modern Chinese poets lack language ability and skills as a result of their childhood learning. However, many modern poets have no creative inspiration, and the author's model can help them. They can look at this model when they choose words and phrases and they can write works based on the poems they already have, and they can write their own poems. The importance of poetry lies in the author's thoughts and reflections. It doesn't matter how good AI poetry is. The only thing that matters is for people to see and inspire them.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 29 references that are in the map. First posted on arXiv 4 Nov 2022.

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4 of 29 references.

References · 4

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2019UER: An Open-Source Toolkit for Pre-training Modelscited by 62
  3. 2018Analogical Reasoning on Chinese Morphological and Semantic Relationscited by 50
  4. 2016Chinese Poetry Generation with Planning based Neural Networkcited by 22

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