@inproceedings{zeng-etal-2024-barking-right,
title = "{``}Barking up the Right Tree{''}, a {GAN}-Based Pun Generation Model through Semantic Pruning",
author = "Zeng, JingJie and
Yang, Liang and
Kang, Jiahao and
Diao, Yufeng and
Yang, Zhihao and
Lin, Hongfei",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.191",
pages = "2119--2131",
abstract = "In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model{'}s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.",
}
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%0 Conference Proceedings
%T “Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning
%A Zeng, JingJie
%A Yang, Liang
%A Kang, Jiahao
%A Diao, Yufeng
%A Yang, Zhihao
%A Lin, Hongfei
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zeng-etal-2024-barking-right
%X In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model’s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.
%U https://aclanthology.org/2024.lrec-main.191
%P 2119-2131
Markdown (Informal)
[“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning](https://aclanthology.org/2024.lrec-main.191) (Zeng et al., LREC-COLING 2024)
ACL
- JingJie Zeng, Liang Yang, Jiahao Kang, Yufeng Diao, Zhihao Yang, and Hongfei Lin. 2024. “Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2119–2131, Torino, Italia. ELRA and ICCL.