Flexible and Creative Chinese Poetry Generation Using Neural Memory
This work addresses the challenge of enhancing creativity in poetry generation for natural language processing applications, though it appears incremental by building on existing sequence-to-sequence models.
The paper tackled the problem of generating creative Chinese poetry by proposing a memory-augmented neural model that balances linguistic rules and aesthetic innovation, resulting in flexible poem generation with different styles.
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.