CleanComedy: Creating Friendly Humor through Generative Techniques

• URL: http://arxiv.org/abs/2412.09203v1
• Date: Thu, 12 Dec 2024 11:57:59 GMT
• Title: CleanComedy: Creating Friendly Humor through Generative Techniques
• Authors: Dmitry Vikhorev, Daria Galimzianova, Svetlana Gorovaia, Elizaveta Zhemchuzhina, Ivan P. Yamshchikov,
• Abstract summary: This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets.
• Score: 5.720553544629197
• License:
• Abstract: Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes collected from various sources. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets.

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