# M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

arXiv: https://arxiv.org/abs/2108.01260 · HTML: https://mlsanity.com/paper/2108.01260/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 3 Aug 2021.

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

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Humor Detection: A Transformer Gets the Last Laugh](https://mlsanity.com/paper/1909.00252/) (2019)
- [Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System](https://mlsanity.com/paper/1806.05513/) (2018)
- ["Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework](https://mlsanity.com/paper/2006.00578/) (2020)
- [Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System](https://mlsanity.com/paper/1806.05600/) (2018)

## Built on by (most cited first, 7 of 7)

- [XMeCap: Meme Caption Generation with Sub-Image Adaptability](https://mlsanity.com/paper/2407.17152/) (2024)
- [StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos](https://mlsanity.com/paper/2505.18903/) (2025)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [HumorDB: Can AI understand graphical humor?](https://mlsanity.com/paper/2406.13564/) (2024)
- [Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results](https://mlsanity.com/paper/2209.14272/) (2022)
- [Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-form Video Humor Detection](https://mlsanity.com/paper/2402.09055/) (2024)
- [On the Wings of Imagination: Conflicting Script-based Multi-role Framework for Humor Caption Generation](https://mlsanity.com/paper/2602.06423/) (2026)

## Abstract (arXiv)

Humor recognition in conversations is a challenging task that has recently gained popularity due to its importance in dialogue understanding, including in multimodal settings (i.e., text, acoustics, and visual). The few existing datasets for humor are mostly in English. However, due to the tremendous growth in multilingual content, there is a great demand to build models and systems that support multilingual information access. To this end, we propose a dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series "Shrimaan Shrimati Phir Se". Each utterance is annotated with humor/non-humor labels and encompasses acoustic, visual, and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations. The empirical results on M2H2 dataset demonstrate that multimodal information complements unimodal information for humor recognition. The dataset and the baselines are available at http://www.iitp.ac.in/~ai-nlp-ml/resources.html and https://github.com/declare-lab/M2H2-dataset.