# Reasoning with Sarcasm by Reading In-between

arXiv: https://arxiv.org/abs/1805.02856 · HTML: https://mlsanity.com/paper/1805.02856/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 30 of 38)

- [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://mlsanity.com/paper/2203.06419/) (2022)
- [Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement](https://mlsanity.com/paper/2210.03501/) (2022)
- [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2306.16650/) (2023)
- [Interpretable Multimodal Misinformation Detection with Logic Reasoning](https://mlsanity.com/paper/2305.05964/) (2023)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement](https://mlsanity.com/paper/2409.19242/) (2024)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)
- [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://mlsanity.com/paper/2104.09261/) (2021)
- [Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues](https://mlsanity.com/paper/2211.11049/) (2022)
- [Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue](https://mlsanity.com/paper/2402.03658/) (2024)
- [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)
- [InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection](https://mlsanity.com/paper/2406.16464/) (2024)
- [CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models](https://mlsanity.com/paper/2506.08430/) (2025)
- [FiLMing Multimodal Sarcasm Detection with Attention](https://mlsanity.com/paper/2110.00416/) (2021)
- [Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://mlsanity.com/paper/2110.04001/) (2021)
- [Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2502.07391/) (2025)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://mlsanity.com/paper/2211.16883/) (2022)
- [BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts](https://mlsanity.com/paper/2309.03658/) (2023)
- [Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2408.02595/) (2024)
- [SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection](https://mlsanity.com/paper/2508.06803/) (2025)
- [World model inspired sarcasm reasoning with large language model agents](https://mlsanity.com/paper/2512.24329/) (2025)
- [How Effective is Incongruity? Implications for Code-mix Sarcasm Detection](https://mlsanity.com/paper/2202.02702/) (2022)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)
- [A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection](https://mlsanity.com/paper/2503.11838/) (2025)
- [MCFNet: A Multimodal Collaborative Fusion Network for Fine-Grained Semantic Classification](https://mlsanity.com/paper/2505.23365/) (2025)
- [RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection](https://mlsanity.com/paper/2601.17002/) (2026)
- [Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media](https://mlsanity.com/paper/2101.05875/) (2021)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English](https://mlsanity.com/paper/2203.02244/) (2022)

## Abstract (arXiv)

Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.