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 generating poems. In
addition, topics of poems are usually represented by the words from the collected poems in the training
corpus. But as we know, the words used in poems, especially poems written in ancient time, are different
from modern languages. As a consequence, the existing methods may fail to generate meaningful poems
if a user wants to write a poem for a modern term (e.g., Barack Obama).
In this paper, we propose a novel poetry generating method which generates poems in a two-stage
procedure: the contents of poems (“what to say”) are first explicitly planned, and then surface realization
(“how to say”) is conducted. Given a user’s writing intent which can be a set of keywords, a sentence or
This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://
creativecommons.org/licenses/by/4.0/
arXiv:1610.09889v2 [cs.CL] 7 Dec 2016
剩余10页未读，继续阅读
资源评论
