# Humor Detection: A Transformer Gets the Last Laugh

arXiv: https://arxiv.org/abs/1909.00252 · HTML: https://mlsanity.com/paper/1909.00252/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 30 of 46)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge](https://mlsanity.com/paper/2101.00376/) (2021)
- [Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models](https://mlsanity.com/paper/2306.04746/) (2023)
- [ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models](https://mlsanity.com/paper/2306.04563/) (2023)
- [ExPUNations: Augmenting Puns with Keywords and Explanations](https://mlsanity.com/paper/2210.13513/) (2022)
- [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://mlsanity.com/paper/2204.12708/) (2022)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs](https://mlsanity.com/paper/2502.20356/) (2025)
- ["So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy](https://mlsanity.com/paper/2110.12765/) (2021)
- [SoMeLVLM: A Large Vision Language Model for Social Media Processing](https://mlsanity.com/paper/2402.13022/) (2024)
- [Evaluating Decision Models for Text Annotation in Computational Social Science](https://mlsanity.com/paper/2609.24574/) (2026)
- [Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?](https://mlsanity.com/paper/2105.09142/) (2021)
- [Humor Mechanics: Advancing Humor Generation with Multistep Reasoning](https://mlsanity.com/paper/2405.07280/) (2024)
- [M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations](https://mlsanity.com/paper/2108.01260/) (2021)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Combining Humor and Sarcasm for Improving Political Parody Detection](https://mlsanity.com/paper/2205.03313/) (2022)
- [Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes](https://mlsanity.com/paper/2507.13335/) (2025)
- [Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation](https://mlsanity.com/paper/2510.00567/) (2025)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity](https://mlsanity.com/paper/2211.01889/) (2022)
- [The Naughtyformer: A Transformer Understands Offensive Humor](https://mlsanity.com/paper/2211.14369/) (2022)
- [CleanComedy: Creating Friendly Humor through Generative Techniques](https://mlsanity.com/paper/2412.09203/) (2024)
- [Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results](https://mlsanity.com/paper/2209.14272/) (2022)
- [Mining Effective Features Using Quantum Entropy for Humor Recognition](https://mlsanity.com/paper/2302.03716/) (2023)
- [Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-form Video Humor Detection](https://mlsanity.com/paper/2402.09055/) (2024)
- [HUMORCHAIN: Theory-Guided Multi-Stage Reasoning for Interpretable Multimodal Humor Generation](https://mlsanity.com/paper/2511.21732/) (2025)
- [On the Wings of Imagination: Conflicting Script-based Multi-role Framework for Humor Caption Generation](https://mlsanity.com/paper/2602.06423/) (2026)
- [LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification](https://mlsanity.com/paper/2408.06335/) (2024)
- [RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science](https://mlsanity.com/paper/2408.08217/) (2024)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)

## Abstract (arXiv)

Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model's increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset.