A New Automatic Chinese Poetry Generation Model Based On Neural Network

Abstract

Chinese ancient poetry has been a favorite literary genre for thousands of years. Nowadays, it is still read and ancient Chinese poets are honored. Recently, many websites provide automatic Chinese poetry generation service based on neural network. My research goal is to improve the quality of the poetry generation, i.e., making it as close as possible to the real poetry written by poets. To achieve this goal, I propose a new context-aware Chinese poetry generation method based on sequence-to-sequence framework. I generate a new concept called keyword team which is a combination of all the keywords to capture the context of the Chinese poetry. Then I use the keyword, the keyword team and the previously generated lines to generate the current line in a poem. I have already implemented the new context-aware Chinese poetry generation model called KPG (Keyword team based Poetry Generation). I have compared this method with state-of-the-art ones using loss function and human judgement. The comprehensive evaluation result show that our proposed method outperforms the state-of-the-art ones.