Flexible and Creative Chinese Poetry Generation Using Neural Memory
Jiyuan Zhang, Yang Feng, Dong Wang, Yang Wang, Andrew Abel, Shiyue Zhang, Andi Zhang
Abstract
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.- Anthology ID:
- P17-1125
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1364–1373
- Language:
- URL:
- https://aclanthology.org/P17-1125
- DOI:
- 10.18653/v1/P17-1125
- Cite (ACL):
- Jiyuan Zhang, Yang Feng, Dong Wang, Yang Wang, Andrew Abel, Shiyue Zhang, and Andi Zhang. 2017. Flexible and Creative Chinese Poetry Generation Using Neural Memory. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1364–1373, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Flexible and Creative Chinese Poetry Generation Using Neural Memory (Zhang et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P17-1125.pdf