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。