| 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. |
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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. |
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. |
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. |
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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. |
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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. |
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 . |
“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. |
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews (D19-55)
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| Challenge: | sarcasm, humor, hate speech, and sentiment are a complex language attribute . sentiment classification models are used for complex language understanding tasks . |
| Approach: | They propose a two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment from online reviews and feeds them to inform sentiment classification. |
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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. |
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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. |