@inproceedings{zhipeng-etal-2019-jiuge,
title = "{J}iuge: A Human-Machine Collaborative {C}hinese Classical Poetry Generation System",
author = "Zhipeng, Guo and
Yi, Xiaoyuan and
Sun, Maosong and
Li, Wenhao and
Yang, Cheng and
Liang, Jiannan and
Chen, Huimin and
Zhang, Yuhui and
Li, Ruoyu",
editor = "Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-3005",
doi = "10.18653/v1/P19-3005",
pages = "25--30",
abstract = "Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.",
}
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%0 Conference Proceedings
%T Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
%A Zhipeng, Guo
%A Yi, Xiaoyuan
%A Sun, Maosong
%A Li, Wenhao
%A Yang, Cheng
%A Liang, Jiannan
%A Chen, Huimin
%A Zhang, Yuhui
%A Li, Ruoyu
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhipeng-etal-2019-jiuge
%X Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.
%R 10.18653/v1/P19-3005
%U https://aclanthology.org/P19-3005
%U https://doi.org/10.18653/v1/P19-3005
%P 25-30
Markdown (Informal)
[Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System](https://aclanthology.org/P19-3005) (Zhipeng et al., ACL 2019)
ACL
- Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, and Ruoyu Li. 2019. Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 25–30, Florence, Italy. Association for Computational Linguistics.