# ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor

arXiv: https://arxiv.org/abs/2004.12765 · HTML: https://mlsanity.com/paper/2004.12765/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 18 of 18)

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- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation](https://mlsanity.com/paper/2403.11757/) (2024)
- [A Two-Model Approach for Humour Style Recognition](https://mlsanity.com/paper/2410.12842/) (2024)
- [Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2504.07521/) (2025)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis](https://mlsanity.com/paper/2501.02891/) (2025)
- [Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition](https://mlsanity.com/paper/2502.04960/) (2025)
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- [A Survey of Pun Generation: Datasets, Evaluations and Methodologies](https://mlsanity.com/paper/2507.04793/) (2025)
- [Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models](https://mlsanity.com/paper/2510.18454/) (2025)
- [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)
- [SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter](https://mlsanity.com/paper/2605.28084/) (2026)
- [Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification](https://mlsanity.com/paper/2403.06023/) (2024)
- [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://mlsanity.com/paper/2604.17255/) (2026)
- [KARMA: Karma-Aligned Reward Model Adaptation](https://mlsanity.com/paper/2605.26738/) (2026)

## Abstract (arXiv)

Automation of humor detection and rating has interesting use cases in modern technologies, such as humanoid robots, chatbots, and virtual assistants. In this paper, we propose a novel approach for detecting and rating humor in short texts based on a popular linguistic theory of humor. The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one. The embeddings are fed to separate lines of hidden layers in a neural network (one line for each sentence) to extract latent features. At last, the parallel lines are concatenated to determine the congruity and other relationships between the sentences and predict the target value. We accompany the paper with a novel dataset for humor detection consisting of 200,000 formal short texts. In addition to evaluating our work on the novel dataset, we participated in a live machine learning competition focused on rating humor in Spanish tweets. The proposed model obtained F1 scores of 0.982 and 0.869 in the humor detection experiments which outperform general and state-of-the-art models. The evaluation performed on two contrasting settings confirm the strength and robustness of the model and suggests two important factors in achieving high accuracy in the current task: 1) usage of sentence embeddings and 2) utilizing the linguistic structure of humor in designing the proposed model.