A new context-aware approach for automatic Chinese poetry generation

Abstract

Chinese poetry has been a favorite literary genre for thousands of years. Chinese ancient poetry is still being read and practiced, and many famous ancient Chinese poets are honored and adorned. Recently, deep learning has been widely adopted for poetry generation. In this paper, we present a new context-aware Chinese poetry generation method based on sequence-to-sequence framework. We generate a new concept called keyword team, which is a combination of all the keywords to capture the context of the Chinese poetry. Then we use the keyword, the keyword team and the previously generated lines to generate the present line in the poetry. We find that, by including keyword teams into the generation of the poetry, it can additionally perceive the keywords of preceding and succeeding lines to generate the present line, which can effectively improve the adhesion among the overall lines. The comprehensive evaluation results show that our proposed model outperforms many of the state-of-the-art poetry generation models.