mlsanityData to 14 Oct 2026

The Naughtyformer: A Transformer Understands Offensive Humor

arXiv 2211.14369 ↗First posted 25 Nov 2022cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

9 of 20 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2020Longformer: The Long-Document Transformercited by 3,413
  5. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  6. 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
  7. 2019Humor Detection: A Transformer Gets the Last Laughcited by 46
  8. 2018Detecting Offensive Language in Tweets Using Deep Learningcited by 23
  9. 2021Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?cited by 9

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Citing papers per year: 2024: 4; 2025: 1.

5 papers.