  With rapid development of neural networks, deep-learning has been extended to
various natural language generation fields, such as machine translation,
dialogue generation and even literature creation. In this paper, we propose a
theme-aware language generation model for Chinese music lyrics, which improves
the theme-connectivity and coherence of generated paragraphs greatly. A
multi-channel sequence-to-sequence (seq2seq) model encodes themes and previous
sentences as global and local contextual information. Moreover, attention
mechanism is incorporated for sequence decoding, enabling to fuse context into
predicted next texts. To prepare appropriate train corpus, LDA (Latent
Dirichlet Allocation) is applied for theme extraction. Generated lyrics is
grammatically correct and semantically coherent with selected themes, which
offers a valuable modelling method in other fields including multi-turn
chatbots, long paragraph generation and etc.
