  Recent studies in sequence-to-sequence learning demonstrate that RNN
encoder-decoder structure can successfully generate Chinese poetry. However,
existing methods can only generate poetry with a given first line or user's
intent theme. In this paper, we proposed a three-stage multi-modal Chinese
poetry generation approach. Given a picture, the first line, the title and the
other lines of the poem are successively generated in three stages. According
to the characteristics of Chinese poems, we propose a hierarchy-attention
seq2seq model which can effectively capture character, phrase, and sentence
information between contexts and improve the symmetry delivered in poems. In
addition, the Latent Dirichlet allocation (LDA) model is utilized for title
generation and improve the relevance of the whole poem and the title. Compared
with strong baseline, the experimental results demonstrate the effectiveness of
our approach, using machine evaluations as well as human judgments.
