Motivated by the recent progresses on machine learning-based models that
learn artistic styles, in this paper we focus on the problem of poem
generation. This is a challenging task in which the machine has to capture the
linguistic features that strongly characterize a certain poet, as well as the
semantics of the poet's production, that are influenced by his personal
experiences and by his literary background. Since poetry is constructed using
syllables, that regulate the form and structure of poems, we propose a
syllable-based neural language model, and we describe a poem generation
mechanism that is designed around the poet style, automatically selecting the
most representative generations. The poetic work of a target author is usually
not enough to successfully train modern deep neural networks, so we propose a
multi-stage procedure that exploits non-poetic works of the same author, and
also other publicly available huge corpora to learn syntax and grammar of the
target language. We focus on the Italian poet Dante Alighieri, widely famous
for his Divine Comedy. A quantitative and qualitative experimental analysis of
the generated tercets is reported, where we included expert judges with strong
background in humanistic studies. The generated tercets are frequently
considered to be real by a generic population of judges, with relative
difference of 56.25\% with respect to the ones really authored by Dante, and
expert judges perceived Dante's style and rhymes in the generated text.

本文探讨了基于神经语言模型和音节的诗歌生成问题，在 Dante Alighieri 的作品中进行了实验分析，结果表明生成的三行诗在句法、语法和诗歌风格上与 Dante 的真正作品相差不大。

神经诗歌：使用音节生成诗歌的学习

Neural Poetry: Learning to Generate Poems using Syllables

It has been shown that Chinese poems can be successfully generated by
sequence-to-sequence neural models, particularly with the attention mechanism.
A potential problem of this approach, however, is that neural models can only
learn abstract rules, while poem generation is a highly creative process that
involves not only rules but also innovations for which pure statistical models
are not appropriate in principle. This work proposes a memory-augmented neural
model for Chinese poem generation, where the neural model and the augmented
memory work together to balance the requirements of linguistic accordance and
aesthetic innovation, leading to innovative generations that are still
rule-compliant. In addition, it is found that the memory mechanism provides
interesting flexibility that can be used to generate poems with different
styles.

使用增强记忆的神经网络模型在中国古诗文的生成中，通过将神经网络和增强内存结合使用，来平衡规则与美学，实现符合规范和有创意的诗歌生成，并提供了生成不同风格诗歌的灵活性。

利用神经记忆实现灵活创新的中国诗歌生成

Flexible and Creative Chinese Poetry Generation Using Neural Memory

Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of art generation. We particularly focus on traditional Chinese poetry,
and show that machines can do as well as many contemporary poets and weakly
pass the Feigenbaum Test, a variant of Turing test in professional domains. Our
method is based on an attention-based recurrent neural network, which accepts a
set of keywords as the theme and generates poems by looking at each keyword
during the generation. A number of techniques are proposed to improve the
model, including character vector initialization, attention to input and
hybrid-style training. Compared to existing poetry generation methods, our
model can generate much more theme-consistent and semantic-rich poems.

本文通过基于注意力机制的循环神经网络，利用一组关键字作为主题，生成类似于人类古诗词的诗歌，方法包括字符向量初始化、注重输入和混合式训练等技术。相比于现有的诗歌生成方法，我们的模型生成的诗歌与主题更加一致，语义更加丰富。