Chinese Poetry Generation with Flexible Styles

TL;DR

This paper introduces a memory-augmented neural model that enables the generation of Chinese poetry with specific styles, including those of particular poets or eras, by leveraging stored style-specific fragments.

Contribution

The work presents a novel memory-augmented neural approach that allows for flexible style-specific Chinese poetry generation, addressing limitations of previous models.

Findings

Successfully generates poems matching specific styles.

Demonstrates style adaptation for individual poets and eras.

Outperforms baseline models in style accuracy.

Abstract

Research has shown that sequence-to-sequence neural models, particularly those with the attention mechanism, can successfully generate classical Chinese poems. However, neural models are not capable of generating poems that match specific styles, such as the impulsive style of Li Bai, a famous poet in the Tang Dynasty. This work proposes a memory-augmented neural model to enable the generation of style-specific poetry. The key idea is a memory structure that stores how poems with a desired style were generated by humans, and uses similar fragments to adjust the generation. We demonstrate that the proposed algorithm generates poems with flexible styles, including styles of a particular era and an individual poet.