# StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos

arXiv: https://arxiv.org/abs/2505.18903 · HTML: https://mlsanity.com/paper/2505.18903/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild](https://mlsanity.com/paper/2401.04210/) (2024)
- [M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations](https://mlsanity.com/paper/2108.01260/) (2021)

## Built on by (most cited first, 9 of 9)

- [MTLLFM: Multimodal-Temporal Laughter Localization: UR-FUNNY-Temporal and SMILE-Temporal Benchmarks with an Adaptive Multimodal Fusion Model](https://mlsanity.com/paper/2605.25409/) (2026)
- [Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor](https://mlsanity.com/paper/2603.17759/) (2026)
- [Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing](https://mlsanity.com/paper/2603.21803/) (2026)
- [ManzaiSet: A Multimodal Dataset of Viewer Responses to Japanese Manzai Comedy](https://mlsanity.com/paper/2510.18014/) (2025)
- [MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method](https://mlsanity.com/paper/2605.06309/) (2026)
- [Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges](https://mlsanity.com/paper/2607.19011/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [TAKE 85: Testing Audiovisual filmmaKer's intEnt across 85 Hours of Film](https://mlsanity.com/paper/2608.30068/) (2026)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)

## Abstract (arXiv)

Aiming towards improving current computational models of humor detection, we propose a new multimodal dataset of stand-up comedies, in seven languages: English, French, Spanish, Italian, Portuguese, Hungarian and Czech. Our dataset of more than 330 hours, is at the time of writing the biggest available for this type of task, and the most diverse. The whole dataset is automatically annotated in laughter (from the audience), and the subpart left for model validation is manually annotated. Contrary to contemporary approaches, we do not frame the task of humor detection as a binary sequence classification, but as word-level sequence labeling, in order to take into account all the context of the sequence and to capture the continuous joke tagging mechanism typically occurring in natural conversations. As par with unimodal baselines results, we propose a method for e propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. Our code and data are available online: https://tinyurl.com/EMNLPHumourStandUpPublic