| Challenge: | Existing methods to collect self-labeled data for irony detection are based on false-alarm hashtags. |
| Approach: | They propose a neural network-based model which disambiguates hashtag usages and prunes the self-labeled training data. |
| Outcome: | The proposed model outperforms the models trained on the less but cleaner training instances. |
Similar Papers
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
Copied to clipboard
| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for detecting intended sarcasm have shown low performance compared to previous studies. |
| Approach: | They propose a dataset of tweets labeled for intended sarcasm by their authors . they aim to encourage future NLP research to develop methods for detecting sarkasmus in text as intended by the authors of the text . |
| Outcome: | The proposed model shows that existing methods are biased or obvious and sarcasm could be understudied. |
Tackling Irony Detection using Ensemble Classifiers (2022.lrec-1)
Copied to clipboard
| Challenge: | Automated approaches to irony detection still fall short of what one would consider desirable performance. |
| Approach: | They propose to use transformer-based approaches to automate irony detection in social media . they propose to augmentation training data to address the binary and fine-grained problem . |
| Outcome: | The proposed methods improve performance over baselines and are not decisive for good results. |
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
Copied to clipboard
| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
Perceived and Intended Sarcasm Detection with Graph Attention Networks (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors, but social studies suggest that the relationship between the author and the audience can be equally relevant for the sarkasmal usage and interpretation. |
| Approach: | They propose a framework leveraging a user context from their historical tweets together with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
| Outcome: | The proposed framework combines a user context from their historical tweets with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
Sarcasm Target Identification: Dataset and An Introductory Approach (L18-1)
Copied to clipboard
| Challenge: | Past work on sarcasm detection has focused on identifying the sarcasm target of ridicule in a sarkastic text. |
| Approach: | They propose a task of extracting the sarcastic target of ridicule from a sarcastical text using a manually annotated dataset and an automatic approach. |
| Outcome: | The proposed approach establishes the viability of sarcasm target identification and will serve as a baseline for future work. |
HashSet - A Dataset For Hashtag Segmentation (2022.lrec-1)
Copied to clipboard
| Challenge: | Hashtag segmentation is the task of breaking a hashtag into constituent tokens . hashtags are often written in unique ways, including spelling variations, and special characters. |
| Approach: | They propose a dataset that breaks hashtags into constituent tokens to train and validate models. |
| Outcome: | The proposed dataset provides an alternate set of hashtags to build and validate hashtag segmentation models. |
Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)
Copied to clipboard
Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso, Farah Benamara
| Challenge: | Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text. |
| Approach: | They propose to exploit linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
| Outcome: | The proposed method exploits linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
Sarcasm Detection in a Disaster Context (2024.lrec-main)
Copied to clipboard
| Challenge: | During natural disasters, people often use social media platforms to express contempt or sarcasm . despite being widely researched as an NLP task, sarkasmatic detection has not been explored in a specific context . |
| Approach: | They propose a dataset of 15,000 tweets annotated for intended sarcasm . they propose sarkasmatic detection using pre-trained language models . |
| Outcome: | The proposed model can obtain as much as 0.70 F1 on the dataset. |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
Copied to clipboard
| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |