Abstract
We take the generation of Chinese classical poetry as a sequence-to-sequence learning problem, and investigate the suitability of recurrent neural network (RNN) for poetry generation task by various qualitative analyses. Then we build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a keyword as input. Our system can learn semantic meaning within a single sentence, semantic relevance among sentences in a poem, and the use of structural, rhythmical and tonal patterns jointly, without utilizing any constraint templates. Experimental results show that our system outperforms other competitive systems.
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Yi, X., Li, R., Sun, M. (2017). Generating Chinese Classical Poems with RNN Encoder-Decoder. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_18
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DOI: https://doi.org/10.1007/978-3-319-69005-6_18
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