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.

本研究使用 BART 和其他预训练模型，提出 FS2TEXT 和 RR2TEXT 以生成韵律诗和特定风格的诗歌文本，并解决了用户写作意图逐渐降低所生成的诗歌文本相关性的问题。同时，运用此模型进行的 AI 诗歌图灵测试显示高水平的诗歌爱好者不能区分 AI 生成的诗歌与人类的诗歌，显示出此模型在诗歌生成方面的出色表现。此模型有望帮助那些缺乏语言能力和创作灵感的现代诗人。