| 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. |
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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 . |
Corpora and Baselines for Humour Recognition in Portuguese (2020.lrec-1)
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| Challenge: | Existing work on the recognition of verbal humour in Portuguese has not been done . humor recognition is a sign of fluency in a language, and is not yet widely used in other languages. |
| Approach: | They propose to create three corpora covering two styles of humour and four sources of non-humorous text that are used for testing computational models. |
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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. |
Mining Effective Features Using Quantum Entropy for Humor Recognition (2023.findings-eacl)
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| Challenge: | Existing studies on humor recognition do not understand the mechanisms that generate humor. |
| Approach: | They propose to use quantum entropy to represent the semantic uncertainty of the setup and punchline as features for humor recognition. |
| Outcome: | The proposed features are more effective than baselines for recognizing humorous and non-humorous texts on the SemEval2021 task 7 dataset. |
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition (2021.acl-short)
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| Challenge: | Existing work on humor recognition does not examine the actual joke mechanism . a recent study focused on humor-specific stylistic features, but few have tried to establish a connection between them and humor theories. |
| Approach: | They propose to model the set-up and punchline as part developing semantic uncertainty and disrupt audience expectations. |
| Outcome: | The proposed features can tell jokes from non-jokes, compared with baselines. |
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. |
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)
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| Challenge: | Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor. |
| Approach: | They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality. |
| Outcome: | The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions. |
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. |
Humor Detection: A Transformer Gets the Last Laugh (D19-1)
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| 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. |