Papers with Humor
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements (2021.acl-long)
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| Challenge: | Humor is an important social phenomenon, serving complex social and psychological functions. |
| Approach: | They propose a novel algorithm for automatically detecting funny scientific papers . they use a dataset containing thousands of funny papers to learn classifiers . |
| Outcome: | The proposed task is based on a dataset containing thousands of funny scientific papers . it is a novel task that can be automated and improves on existing methods . |
Humor Recognition Using Deep Learning (N18-2)
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| Challenge: | Humor is an essential but most fascinating element in personal communication. |
| Approach: | They propose a convolutional neural network with extensive filter size and filter number to increase the depth of networks. |
| Outcome: | The proposed model outperforms existing models on accuracy, precision and recall . the proposed model can learn to distinguish between humorous and nonhumorous texts . |
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition (2020.acl-main)
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| Challenge: | Identifying and modeling puns is challenging as they involve implicit semantic or phonological tricks. |
| Approach: | They propose a method to detect puns in a sentence and then locate them in it . they propose to capture phonetic associations between the context and phonetic symbols . |
| Outcome: | The proposed method outperforms state-of-the-art methods in pun detection and location tasks. |
Modeling Sentiment Association in Discourse for Humor Recognition (P18-2)
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| Challenge: | Existing work on sentiment information is limited to the number of emotional words. |
| Approach: | They propose to model sentiment association between discourse units to indicate how punchline breaks expectation of setup. |
| Outcome: | The proposed model shows that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. |
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor (D19-1)
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Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, Mohammed (Ehsan) Hoque
| Challenge: | Humor is a unique and creative communicative behavior often displayed during social interactions. |
| Approach: | They present a dataset that allows to model multimodal language used in expressing humor using text, visual and acoustic communication. |
| Outcome: | The proposed framework opens the door to understanding multimodal language used in expressing humor. |
“I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese (2022.findings-emnlp)
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| Challenge: | a recent study has focused on how to recognize punchlines from dialogues, but has neglected character information. |
| Approach: | They propose a character-fusion conversational humor recognition model that uses character information to recognize punchlines from dialogue. |
| Outcome: | The proposed model improves performance on Chinese sitcoms corpus and punchline identification. |
Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game (2022.findings-emnlp)
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| Challenge: | Humor is an inherently social phenomenon, with utterances shaped by what is socially and culturally accepted. |
| Approach: | They propose a dataset of cards Against humanity, including 785K unique jokes, and train machine learning models to predict the winning joke per game. |
| Outcome: | The proposed model performs twice as well as random on the more difficult task of judging novel cards, with the context having little impact. |
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting (2022.coling-1)
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| Challenge: | Humor is an essential aspect of daily conversation, and people try to provoke humor in their talks. |
| Approach: | They propose a multitask framework that annotates Hindi utterances with sentiment and emotion classes. |
| Outcome: | The proposed framework improves on the recently released Hindi Humor dataset . it takes sentiment and emotion into account to understand humor . |
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)
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| Challenge: | Humor plays important role in human communication, which makes it important problem for natural language processing. |
| Approach: | They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset . |
| Outcome: | The proposed scheme gives scenarios of how humor arises in text . it contains key words that trigger humor, character relationship, scene, and humor categories . |
The rJokes Dataset: a Large Scale Humor Collection (2020.lrec-1)
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| Challenge: | Humor is a complex language phenomenon that depends upon many factors, including topic, date, and recipient. |
| Approach: | They compile a large scale humor dataset from the Reddit r/Jokes subreddit. |
| Outcome: | The proposed dataset provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. |
“What do you call a dog that is incontrovertibly true? Dogma”: Testing LLM Generalization through Humor (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong performance in NLP tasks like text summarization and question answering. |
| Approach: | They propose a new humor-based question-answering benchmark to assess LLMs’ reasoning through carefully crafted puns. |
| Outcome: | Experiments on pun comprehension, resolution, and generation reveal that most LLMs struggle with generalization, even on simple tasks, consistently underperforming the human baseline. |
BottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle (2025.findings-acl)
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| Challenge: | Humor is an effective communication tool that can manifest in various forms, including puns, exaggerated facial expressions, absurd behaviors, and incongruities. |
| Approach: | They propose a method that elicits relevant world knowledge from vision and language models and refines it to generate an explanation of the humor in an unsupervised manner. |
| Outcome: | The proposed method can be adapted for additional tasks that can benefit from eliciting and conditioning on relevant world knowledge. |
Puntuguese: A Corpus of Puns in Portuguese with Micro-edits (2024.lrec-main)
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Marcio Lima Inacio, Gabriela Wick-Pedro, Renata Ramisch, Luís Espírito Santo, Xiomara S. Q. Chacon, Roney Santos, Rogério Sousa, Rafael Anchiêta, Hugo Goncalo Oliveira
| Challenge: | Existing corpus of punning humor in Portuguese is unfit for machine learning due to data leakage. |
| Approach: | They propose to use Puntuguese to create a corpus of punning humor in Portuguese that is significantly more difficult to recognize than the previous corpus. |
| Outcome: | The proposed corpus achieves an F1-Score of 68.9% and is significantly more difficult than the previous corpus. |
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor (2026.acl-long)
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Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka, Seungbeen Lee, Jaeyoung Lee, Hyeju Jang, Alice Oh, Youngjae Yu
| Challenge: | Large Language Models (LLMs) absorb social and cultural biases embedded in vast web-scale corpora and are increasingly deployed in high-stakes domains such as hiring, education, and law. |
| Approach: | They propose a framework to investigate counterfactual unfairness through humor by observing how the model’s responses change when we swap who speaks and who is addressed while holding other factors constant. |
| Outcome: | The proposed framework covers humor generation refusal, speaker intention inference, and relational/societal impact prediction tasks. |