# How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

arXiv: https://arxiv.org/abs/2106.03048 · HTML: https://mlsanity.com/paper/2106.03048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"](https://mlsanity.com/paper/1901.03253/) (2019)
- [Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation](https://mlsanity.com/paper/2004.14905/) (2020)
- [Analyzing Political Parody in Social Media](https://mlsanity.com/paper/2004.13878/) (2020)
- [Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops](https://mlsanity.com/paper/1902.02783/) (2019)

## Built on by (most cited first, 3 of 3)

- [Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End](https://mlsanity.com/paper/2212.10522/) (2022)
- [A Two-Model Approach for Humour Style Recognition](https://mlsanity.com/paper/2410.12842/) (2024)
- [MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions](https://mlsanity.com/paper/2609.11322/) (2026)

## Abstract (arXiv)

Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?"). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.