@inproceedings{xu-etal-2018-diversity,
title = "Diversity-Promoting {GAN}: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation",
author = "Xu, Jingjing and
Ren, Xuancheng and
Lin, Junyang and
Sun, Xu",
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/D18-1428/",
doi = "10.18653/v1/D18-1428",
pages = "3940--3949",
abstract = "Existing text generation methods tend to produce repeated and ``boring'' expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for ``novel'' and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines."
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<abstract>Existing text generation methods tend to produce repeated and “boring” expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for “novel” and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines.</abstract>
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%0 Conference Proceedings
%T Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation
%A Xu, Jingjing
%A Ren, Xuancheng
%A Lin, Junyang
%A Sun, Xu
%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 xu-etal-2018-diversity
%X Existing text generation methods tend to produce repeated and “boring” expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for “novel” and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines.
%R 10.18653/v1/D18-1428
%U https://aclanthology.org/D18-1428/
%U https://doi.org/10.18653/v1/D18-1428
%P 3940-3949
Markdown (Informal)
[Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation](https://aclanthology.org/D18-1428/) (Xu et al., EMNLP 2018)
ACL