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The Naughtyformer: A Transformer Understands Offensive Humor

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arxiv 2211.14369 v1 pith:SBBKJWH5 submitted 2022-11-25 cs.CL

classification cs.CL
keywords jokeshumordetectingnaughtyformertasktransformeraudiencebest
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Jokes are intentionally written to be funny, but not all jokes are created the same. Some jokes may be fit for a classroom of kindergarteners, but others are best reserved for a more mature audience. While recent work has shown impressive results on humor detection in text, here we instead investigate the more nuanced task of detecting humor subtypes, especially of the less innocent variety. To that end, we introduce a novel jokes dataset filtered from Reddit and solve the subtype classification task using a finetuned Transformer dubbed the Naughtyformer. Moreover, we show that our model is significantly better at detecting offensiveness in jokes compared to state-of-the-art methods.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CleanComedy: Creating Friendly Humor through Generative Techniques

    cs.CL 2024-12 conditional novelty 6.0 of 10

    CleanComedy delivers cleaner bilingual joke datasets, but its fine-tuned models produce less funny jokes than GPT-4o or unfiltered human jokes.

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