| Challenge: | Existing methods to identify humor in text have been limited to identifying humor in the text. |
| Approach: | They propose a model that learns to identify humorous jokes based on Reddit ratings, and employ a Transformer architecture to learn from sentence context. |
| Outcome: | The proposed model outperforms previous work on humor identification tasks with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset. |
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Recognizing Humour using Word Associations and Humour Anchor Extraction (C18-1)
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| Challenge: | Using humour anchors to improve the performance of humor recognition and interpretation is difficult for computers. |
| Approach: | They propose to use word associations to improve humour recognition models by using humor anchors to improve the performance of semantic features. |
| Outcome: | The proposed models improve the performance of humour recognition and interpretation tasks. |
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 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. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
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| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity (2022.coling-1)
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| Challenge: | Existing methods to generate humor using multimodal data are needed to study the role of humor in human social function. |
| Approach: | They propose a model that automatically detects humor in the Friends TV show using multimodal data and use prerecorded laughter as annotation as it marks humor. |
| Outcome: | The proposed model detects humor 78% of the time and how long the audience’s laughter reaction should last with a mean absolute error of 600 milliseconds. |
You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models (2023.emnlp-main)
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| Challenge: | a recent study shows that there is little research on how models trained on humor datasets generalize and behave in the wild. |
| Approach: | They analyze existing English humor datasets and train RoBERTa-based and Nave Bayes classifiers on them. |
| Outcome: | The proposed models show that they can generalize and behave on humor datasets, but the transferability of the models is poor. |
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy (2022.lrec-1)
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| Challenge: | a lot of computational tools focus on standalone jokes or on occasional humorous sentences during presentations. |
| Approach: | They propose to use stand-up comedy transcripts to extract humor from a larger narrative. |
| Outcome: | The dataset, SCRIPTS, is built using stand-up comedy shows transcripts. |
Exploiting Syntactic Structures for Humor Recognition (C18-1)
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| Challenge: | Using syntactic structure features, we find humor recognition is a kind of style . |
| Approach: | They propose to exploit syntactic structure features to enhance humor recognition . they find syntastic structure features consistently correlate with humor . |
| Outcome: | The proposed method achieves significant improvements compared with baselines. |
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest (2023.acl-long)
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Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi
| Challenge: | Large neural networks can generate jokes, but do they really “understand” humor? a new challenge challenges AI models to match a joke to a cartoon, identify a winning caption, and explain why a winner is funny. |
| Approach: | They propose three tasks based on the New Yorker Cartoon Caption Contest . they aim to match a joke to a cartoon, identify a winning caption and explain why it's funny . |
| Outcome: | The proposed tasks are based on the New Yorker Cartoon Caption Contest . they include matching a joke to a cartoon, identifying a winning caption, and explaining why a funny caption is funny. |
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 . |