# A Two-Model Approach for Humour Style Recognition

arXiv: https://arxiv.org/abs/2410.12842 · HTML: https://mlsanity.com/paper/2410.12842/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 9 Oct 2024.

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

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder](https://mlsanity.com/paper/2205.12035/) (2022)
- [Pun Generation with Surprise](https://mlsanity.com/paper/1904.06828/) (2019)
- [The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress](https://mlsanity.com/paper/2207.05691/) (2022)
- [Pun-GAN: Generative Adversarial Network for Pun Generation](https://mlsanity.com/paper/1910.10950/) (2019)
- [ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor](https://mlsanity.com/paper/2004.12765/) (2020)
- [Impact of annotation modality on label quality and model performance in the automatic assessment of laughter in-the-wild](https://mlsanity.com/paper/2211.00794/) (2022)
- [How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements](https://mlsanity.com/paper/2106.03048/) (2021)

## Built on by (most cited first, 6 of 6)

- [Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis](https://mlsanity.com/paper/2501.02891/) (2025)
- [Psychology-Driven Enhancement of Humour Translation](https://mlsanity.com/paper/2507.09259/) (2025)
- [Serial-Parallel Dual-Path Architecture for Speaking Style Recognition](https://mlsanity.com/paper/2510.11732/) (2025)
- [Investigating Counterfactual Unfairness in LLMs towards Identities through Humor](https://mlsanity.com/paper/2604.18729/) (2026)
- [Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion](https://mlsanity.com/paper/2608.26879/) (2026)
- [MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions](https://mlsanity.com/paper/2609.11322/) (2026)

## Abstract (arXiv)

Humour, a fundamental aspect of human communication, manifests itself in various styles that significantly impact social interactions and mental health. Recognising different humour styles poses challenges due to the lack of established datasets and machine learning (ML) models. To address this gap, we present a new text dataset for humour style recognition, comprising 1463 instances across four styles (self-enhancing, self-deprecating, affiliative, and aggressive) and non-humorous text, with lengths ranging from 4 to 229 words. Our research employs various computational methods, including classic machine learning classifiers, text embedding models, and DistilBERT, to establish baseline performance. Additionally, we propose a two-model approach to enhance humour style recognition, particularly in distinguishing between affiliative and aggressive styles. Our method demonstrates an 11.61% improvement in f1-score for affiliative humour classification, with consistent improvements in the 14 models tested. Our findings contribute to the computational analysis of humour in text, offering new tools for studying humour in literature, social media, and other textual sources.