@inproceedings{goel-etal-2024-automating,
title = "Automating Humor: A Novel Approach to Joke Generation Using Template Extraction and Infilling",
author = "Goel, Mayank and
Krishnamurthy, Parameswari and
Mamidi, Radhika",
editor = "Lalitha Devi, Sobha and
Arora, Karunesh",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-1.51/",
pages = "442--448",
abstract = "This paper presents a novel approach to humor generation in natural language processing by automating the creation of jokes through template extraction and infilling. Traditional methods have relied on predefined templates or neural network models, which either lack complexity or fail to produce genuinely humorous content. Our method introduces a technique to extract templates from existing jokes based on semantic salience and BERT`s attention weights. We then infill these templates using advanced techniques, through BERT and large language models (LLMs) like GPT-4, to generate new jokes. Our results indicate that the generated jokes are novel and human-like, with BERT showing promise in generating funny content and GPT-4 excelling in creating clever jokes. The study contributes to a deeper understanding of humor generation and the potential of AI in creative domains."
}
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%0 Conference Proceedings
%T Automating Humor: A Novel Approach to Joke Generation Using Template Extraction and Infilling
%A Goel, Mayank
%A Krishnamurthy, Parameswari
%A Mamidi, Radhika
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F goel-etal-2024-automating
%X This paper presents a novel approach to humor generation in natural language processing by automating the creation of jokes through template extraction and infilling. Traditional methods have relied on predefined templates or neural network models, which either lack complexity or fail to produce genuinely humorous content. Our method introduces a technique to extract templates from existing jokes based on semantic salience and BERT‘s attention weights. We then infill these templates using advanced techniques, through BERT and large language models (LLMs) like GPT-4, to generate new jokes. Our results indicate that the generated jokes are novel and human-like, with BERT showing promise in generating funny content and GPT-4 excelling in creating clever jokes. The study contributes to a deeper understanding of humor generation and the potential of AI in creative domains.
%U https://aclanthology.org/2024.icon-1.51/
%P 442-448
Markdown (Informal)
[Automating Humor: A Novel Approach to Joke Generation Using Template Extraction and Infilling](https://aclanthology.org/2024.icon-1.51/) (Goel et al., ICON 2024)
ACL