@inproceedings{zhang-etal-2020-improving-adversarial,
title = "Improving Adversarial Text Generation by Modeling the Distant Future",
author = "Zhang, Ruiyi and
Chen, Changyou and
Gan, Zhe and
Wang, Wenlin and
Shen, Dinghan and
Wang, Guoyin and
Wen, Zheng and
Carin, Lawrence",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.227",
doi = "10.18653/v1/2020.acl-main.227",
pages = "2516--2531",
abstract = "Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.",
}
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<abstract>Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.</abstract>
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%0 Conference Proceedings
%T Improving Adversarial Text Generation by Modeling the Distant Future
%A Zhang, Ruiyi
%A Chen, Changyou
%A Gan, Zhe
%A Wang, Wenlin
%A Shen, Dinghan
%A Wang, Guoyin
%A Wen, Zheng
%A Carin, Lawrence
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-improving-adversarial
%X Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.
%R 10.18653/v1/2020.acl-main.227
%U https://aclanthology.org/2020.acl-main.227
%U https://doi.org/10.18653/v1/2020.acl-main.227
%P 2516-2531
Markdown (Informal)
[Improving Adversarial Text Generation by Modeling the Distant Future](https://aclanthology.org/2020.acl-main.227) (Zhang et al., ACL 2020)
ACL
- Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, and Lawrence Carin. 2020. Improving Adversarial Text Generation by Modeling the Distant Future. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2516–2531, Online. Association for Computational Linguistics.