Humor Detection
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Benchmarks
Most implemented
XGBoost: A Scalable Tree Boosting System
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor
Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
Humor Detection: A Transformer Gets the Last Laugh
Papers
CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension
Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor remains challenging because humorous content often depends on subtl…
Humor DetectionBeyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes
Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist. These complexities highlight …
Humor DetectionMixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain scarce. Existing sentiment and sarcasm models largely focus on monolingua…
Humor DetectionGRCF: Two-Stage Groupwise Ranking and Calibration Framework for Multimodal Sentiment Analysis
Most Multimodal Sentiment Analysis research has focused on point-wise regression. While straightforward, this approach is sensitive to label noise and neglects whether one sample is more positive than another, resulting …
Multimodal Sentiment AnalysisSarcasm DetectionHumor DetectionDark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest
Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we curate and analyze a novel corpus of sentences from the Bulwer-Lytton Fic…
Humor DetectionTowards Minimal Causal Representations for Human Multimodal Language Understanding
Human Multimodal Language Understanding (MLU) aims to infer human intentions by integrating related cues from heterogeneous modalities. Existing works predominantly follow a ``learning to attend" paradigm, which maximize…
Multimodal Sentiment AnalysisSarcasm DetectionHumor Detection