@inproceedings{luo-etal-2019-pun,
title = "Pun-{GAN}: Generative Adversarial Network for Pun Generation",
author = "Luo, Fuli and
Li, Shunyao and
Yang, Pengcheng and
Li, Lei and
Chang, Baobao and
Sui, Zhifang and
Sun, Xu",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1336",
doi = "10.18653/v1/D19-1336",
pages = "3388--3393",
abstract = "In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN). It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.",
}
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<abstract>In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN). It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.</abstract>
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%0 Conference Proceedings
%T Pun-GAN: Generative Adversarial Network for Pun Generation
%A Luo, Fuli
%A Li, Shunyao
%A Yang, Pengcheng
%A Li, Lei
%A Chang, Baobao
%A Sui, Zhifang
%A Sun, Xu
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F luo-etal-2019-pun
%X In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN). It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.
%R 10.18653/v1/D19-1336
%U https://aclanthology.org/D19-1336
%U https://doi.org/10.18653/v1/D19-1336
%P 3388-3393
Markdown (Informal)
[Pun-GAN: Generative Adversarial Network for Pun Generation](https://aclanthology.org/D19-1336) (Luo et al., EMNLP-IJCNLP 2019)
ACL
- Fuli Luo, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, and Xu Sun. 2019. Pun-GAN: Generative Adversarial Network for Pun Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3388–3393, Hong Kong, China. Association for Computational Linguistics.