# Finetuning for Sarcasm Detection with a Pruned Dataset

arXiv: https://arxiv.org/abs/2212.12213 · HTML: https://mlsanity.com/paper/2212.12213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 23 Dec 2022.

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

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [On Loss Functions for Deep Neural Networks in Classification](https://mlsanity.com/paper/1702.05659/) (2017)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [iSarcasm: A Dataset of Intended Sarcasm](https://mlsanity.com/paper/1911.03123/) (2019)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Sarcasm Detection using Hybrid Neural Network](https://mlsanity.com/paper/1908.07414/) (2019)
- [UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation](https://mlsanity.com/paper/2204.08198/) (2022)

## Abstract (arXiv)

Sarcasm is a form of irony that involves saying or writing something that is opposite or opposite to what one really means, often in a humorous or mocking way. It is often used to mock or mock someone or something, or to be humorous or amusing. Sarcasm is usually conveyed through tone of voice, facial expressions, or other forms of nonverbal communication, but it can also be indicated by the use of certain words or phrases that are typically associated with irony or humor. Sarcasm detection is difficult because it relies on context and non-verbal cues. It can also be culturally specific, subjective and ambiguous. In this work, we fine-tune the RoBERTa based sarcasm detection model presented in Abaskohi et al. [2022] to get to within 0.02 F1 of the state-of-the-art (Hercog et al. [2022]) on the iSarcasm dataset (Oprea and Magdy [2019]). This performance is achieved by augmenting iSarcasm with a pruned version of the Self Annotated Reddit Corpus (SARC) (Khodak et al. [2017]). Our pruned version is 100 times smaller than the subset of SARC used to train the state-of-the-art model.