Abstract
The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing. Most previous works on automatic Chinese poetry generation focused on improving the coherency among lines. Some work explored style transfer but suffered from expensive expert labeling of poem styles. In this paper, we target on stylistic poetry generation in a fully unsupervised manner for the first time. We propose a novel model which requires no supervised style labeling by incorporating mutual information, a concept in information theory, into modeling. Experimental results show that our model is able to generate stylistic poems without losing fluency and coherency.- Anthology ID:
- D18-1430
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3960–3969
- Language:
- URL:
- https://aclanthology.org/D18-1430
- DOI:
- 10.18653/v1/D18-1430
- Cite (ACL):
- Cheng Yang, Maosong Sun, Xiaoyuan Yi, and Wenhao Li. 2018. Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3960–3969, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (Yang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/D18-1430.pdf