Chinese Poetry Generation with Planning based Neural Network

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1051–1060, Osaka, Japan, December 11-17 2016. Chinese Poetry Generation with Planning based Neural Network Zhe Wang†, Wei He‡, Hua Wu‡, Haiyang Wu‡, Wei Li‡, Haifeng Wang‡, Enhong Chen† †University of Science and Technology of China, Hefei, China ‡Baidu Inc., Beijing, China xiaose@mail.ustc.edu.cn, cheneh@ustc.edu.cn {hewei06, wu hua, wuhaiyang, liwei08, wanghaifeng}@baidu.com Abstract Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user’s writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning- based method can ensure that the generated poem is coherent and semantically consistent with the user’s intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets. 1 Introduction The classical Chinese poetry is a great and important heritage of Chinese culture. During the history of more than two thousand years, millions of beautiful poems are written to praise heroic characters, beautiful scenery, love, friendship, etc. There are different kinds of Chinese classical poetry, such as Tang poetry and Song iambics. Each type of poetry has to follow some specific structural, rhythmical and tonal patterns. Table 1 shows an example of quatrain which was one of the most popular genres of poetry in China. The principles of a quatrain include: The poem consists of four lines and each line has five or seven characters; every character has a particular tone, Ping (the level tone) or Ze (the downward tone); the last character of the second and last line in a quatrain must belong to the same rhyme category (Wang, 2002). With such strict restrictions, the well-written quatrain is full of rhythmic beauty. In recent years, the research of automatic poetry generation has received great attention. Most ap- proaches employ rules or templates (Tosa et al., 2008; Wu et al., 2009; Netzer et al., 2009; Oliveira, 2009; Oliveira, 2012), genetic algorithms (Manurung, 2004; Zhou et al., 2010; Manurung et al., 2012), summarization methods (Yan et al., 2013) and statistical machine translation methods (Jiang and Zhou, 2008; He et al., 2012) to generate poems. More recently, deep learning methods have emerged as a promising discipline, which considers the poetry generation as a sequence-to-sequence generation prob- lem (Zhang and Lapata, 2014; Wang et al., 2016; Yi et al., 2016). These methods usually generate the first line by selecting one line from the dataset of poems according to the user’s writing intents (usually a set of keywords), and the other three lines are generated based on the first line and the previous lines. The user’s writing intent can only affect the first line, and the rest three lines may have no association with the main topic of the poem, which may lead to semantic inconsistency when …

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