Poetry Generation Algorithm with Automatic Expansion of Keyword Semantic Information

At present, most of the poetry generation models use keywords provided by users to generate poems that conform to the rules of rhythm and fluctuations in pitch. Because keywords contain less semantic information, it is difficult to guarantee the quality of generated poems, and the phenomenon of contextual theme shift is likely to occur. In response to this problem, this paper proposes a generative model based on conditional variational autoencoders, which can generate poems that are more in line with keyword descriptions and user satisfaction under the guidance of richer semantic information. By sampling human poetry and introducing additional semantic information related to keywords, the model effectively estimates the prior probability distribution of the conditional variational autoencoder, and generates a prior probability that more closely matches the true distribution. Because this model automatically expands keyword information, it narrows the gap between input and output semantic information, and alleviates the over-translation problem that is common in previous models. Experimental results show that the proposed model has better results than other models in both automatic and human evaluation, successfully reduces the frequency of over-translation problems and improves the fluency of generated poetry. By changing the range of sampling, controlling the writing style of the generated poetry is successfully achieved, which further shows the effectiveness of the algorithm proposed in this paper.