Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation
Chinese poetry is an important part of worldwide culture, and classical and modern sub branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to control the coherence and improve the novelty. In this paper, we propose a generate retrieve then refine paradigm to jointly improve the coherence and novelty. In the first stage, a draft is generated given keywords (i.e. topics) only. The second stage produces a "refining vector" from retrieval lines. At last, we take into consideration both the draft and the "refining vector" to generate a new poem. The draft provides future sentence level information for a line to be generated. Meanwhile, the "refining vector" points out the direction of refinement based on impressive words detection mechanism which can learn good patterns from references and then create new ones via insertion operation. Experimental results on a collected large scale modern Chinese poetry dataset show that our proposed approach can not only generate more coherent poems, but also improve the diversity and novelty.
2020 International Joint Conference on Neural Networks (IJCNN)