CleanComedy: Creating Friendly Humor through Generative Techniques
This addresses the issue of limited and toxic humor datasets for researchers and developers in natural language processing, though it is incremental as it builds on existing data collection and filtering methods.
The paper tackled the problem of humor generation in NLP by creating CleanComedy, a toxicity-filtered corpus of English and Russian jokes, and found that their filtering approach improved humor quality and reduced toxicity in generated jokes.
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.