# "When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity

arXiv: https://arxiv.org/abs/2305.04105 · HTML: https://mlsanity.com/paper/2305.04105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 97 references that are in the map.
- First posted on arXiv 6 May 2023.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://mlsanity.com/paper/1805.05181/) (2018)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [Reasoning with Sarcasm by Reading In-between](https://mlsanity.com/paper/1805.02856/) (2018)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://mlsanity.com/paper/2004.13248/) (2020)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- [Modelling Context with User Embeddings for Sarcasm Detection in Social Media](https://mlsanity.com/paper/1607.00976/) (2016)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [Sarcasm Analysis using Conversation Context](https://mlsanity.com/paper/1808.07531/) (2018)
- [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://mlsanity.com/paper/1610.00883/) (2016)
- [Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media](https://mlsanity.com/paper/2005.11424/) (2020)
- [Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog](https://mlsanity.com/paper/1709.05305/) (2017)
- [Sarcasm Detection using Context Separators in Online Discourse](https://mlsanity.com/paper/2006.00850/) (2020)
- [Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://mlsanity.com/paper/2006.06259/) (2020)
- [Reactive Supervision: A New Method for Collecting Sarcasm Data](https://mlsanity.com/paper/2009.13080/) (2020)
- [NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention](https://mlsanity.com/paper/1804.06657/) (2018)
- [A Robust Deep Ensemble Classifier for Figurative Language Detection](https://mlsanity.com/paper/2107.04372/) (2021)
- [How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation](https://mlsanity.com/paper/2211.10992/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)

## Abstract (arXiv)

Sarcasm is a form of figurative language that serves as a humorous tool for mockery and ridicule. We present a novel architecture for sarcasm generation with emoji from a non-sarcastic input sentence in English. We divide the generation task into two sub tasks: one for generating textual sarcasm and another for collecting emojis associated with those sarcastic sentences. Two key elements of sarcasm are incorporated into the textual sarcasm generation task: valence reversal and semantic incongruity with context, where the context may involve shared commonsense or general knowledge between the speaker and their audience. The majority of existing sarcasm generation works have focused on this textual form. However, in the real world, when written texts fall short of effectively capturing the emotional cues of spoken and face-to-face communication, people often opt for emojis to accurately express their emotions. Due to the wide range of applications of emojis, incorporating appropriate emojis to generate textual sarcastic sentences helps advance sarcasm generation. We conclude our study by evaluating the generated sarcastic sentences using human judgement. All the codes and data used in this study has been made publicly available.