基于双向多层转换编解码的诗自动生成

中国古诗因其严谨的平仄、押韵结构,精练传神的用词成为文本自动生成领域的挑战问题之一.本文基于双向多层转换编解码的空间向量,结合Attention机制建立了循环神经网络结构的诗自动生成模型.为解决自动生成诗的主题发散性问题,模型在生成每句诗时均增加了关键词约束.此外,为了增强诗句之间的对仗性和语义的连贯性,模型双向多层转...

Automatic Generation of Poem based on Two-way Multi-layer Conversion Codec

Chinese ancient poems have become one of the challenges in the field of automatic text generation because of their tonal patterns,rhyming structure,and vivid words.This paper proposes to establish an automatic poem generation model with a recurrent neural network structure.This model is combined with Attention and is based on space vector of two-way multi-layer conversion codec.In order to solve the problem of divergence of automatically generated poems,the model adds keyword constraints when generating each poem.In addition,in order to enhance antithesis between verses and semantic coherence,word embedded input of the model’s two-way multi-layer conversion encoding and decoding adds alignment vector of the poem.The experimental results show that compared with word-embedded poem automatic generation model in which words are converted into vectors,poems generated by BERT-based aligned vector input model proposed in this paper,not only perform better in machine and manual evaluation,but also generate poetic sentences.The correlation between adjacent sentences and the generated verse is also the best.This also further shows that when input vector of the model can fully express the format,content and semantics of the poem,vector representation of the two-way multi-layer conversion codec,the Attention Mechanism+cyclic neural network construction of poem generation model can generate poems closer to human’s works.