Generating Chinese Classical Poems with RNN Encoder-Decoder

By Xiaoyuan Yi, Ruoyu Li, Maosong Sun. 2016

117 citations recorded by the source corpus.

We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input. Our system can jointly learn semantic meaning within a single line, semantic relevance among lines in a poem, and the use of structural, rhythmical and tonal patterns, without utilizing any constraint templates. Experimental results show that our system outperforms other competitive systems. We also find that the attention mechanism can capture the word associations in Chinese classical poetry and inverting target lines in training can improve performance.

Subfields: Artificial Intelligence, Natural Language Processing, Natural Language Generation

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Citations in the corpus

Hafez: an Interactive Poetry Generation System (2017, methodology): “…approach the problem by using grammatical and semantic templates (Oliveira, 2009, 2012) or treating the generation task as a translation/summarization task (Zhou et al., 2009; He et al., 2012; Yan et al., 2013; Zhang and Lapata, 2014; Yi et al., 2016; Wang et al., 2016; Ghazvininejad et al., 2016).”