@inproceedings{li-etal-2018-generating-classical,
title = "Generating Classical {C}hinese Poems via Conditional Variational Autoencoder and Adversarial Training",
author = "Li, Juntao and
Song, Yan and
Zhang, Haisong and
Chen, Dongmin and
Shi, Shuming and
Zhao, Dongyan and
Yan, Rui",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1423",
doi = "10.18653/v1/D18-1423",
pages = "3890--3900",
abstract = "It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.",
}
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%0 Conference Proceedings
%T Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
%A Li, Juntao
%A Song, Yan
%A Zhang, Haisong
%A Chen, Dongmin
%A Shi, Shuming
%A Zhao, Dongyan
%A Yan, Rui
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-generating-classical
%X It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.
%R 10.18653/v1/D18-1423
%U https://aclanthology.org/D18-1423
%U https://doi.org/10.18653/v1/D18-1423
%P 3890-3900
Markdown (Informal)
[Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training](https://aclanthology.org/D18-1423) (Li et al., EMNLP 2018)
ACL