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        <namePart type="given">Arvind</namePart>
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    <abstract>A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. We then leverage commonsense and counterfactual inference to generate hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles with high success rate, intensity, funniness, and creativity.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.136</identifier>
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%0 Conference Proceedings
%T HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge
%A Tian, Yufei
%A Sridhar, Arvind krishna
%A Peng, Nanyun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F tian-etal-2021-hypogen-hyperbole
%X A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. We then leverage commonsense and counterfactual inference to generate hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles with high success rate, intensity, funniness, and creativity.
%R 10.18653/v1/2021.findings-emnlp.136
%U https://aclanthology.org/2021.findings-emnlp.136
%U https://doi.org/10.18653/v1/2021.findings-emnlp.136
%P 1583-1593

Markdown (Informal)

[HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://aclanthology.org/2021.findings-emnlp.136) (Tian et al., Findings 2021)

• HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge (Tian et al., Findings 2021)

ACL

• Yufei Tian, Arvind krishna Sridhar, and Nanyun Peng. 2021. HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1583–1593, Punta Cana, Dominican Republic. Association for Computational Linguistics.