pwc-archive/papers-with-abstracts huggingface dataset:
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
Authors: Ibrahim Abu Farha, Silviu Vlad Oprea, Steven Wilson, Walid Magdy
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection, Sentence

iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared task contains two languages, English and Arabic, and three subtasks: sarcasm detection, sarcasm category classification, and pairwise sarcasm identification given a sarcastic sentence and its non-sarcastic rephrase. The task received submissions from 60 different teams, with the sarcasm detection task being the most popular. Most of the participating teams utilised pre-trained language models. In this paper, we provide an overview of the task, data, and participating teams.
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm
Authors: Xiyang Du, Dou Hu, Jin Zhi, Lianxin Jiang, Xiaofeng Shi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection

This paper describes the method we utilized in the SemEval-2022 Task 6 iSarcasmEval: Intended Sarcasm Detection In English and Arabic. Our system has achieved 1st in SubtaskB, which is to identify the categories of intended sarcasm. The proposed system integrates multiple BERT-based, RoBERTa-based and BERTweet-based models with finetuning. In this task, we contributed the following: 1) we reveal several large pre-trained models’ performance on tasks coping with the tweet-like text. 2) Our methods prove that we can still achieve excellent results in this particular task without a complex classifier adopting some proper training method. 3) we found there is a hierarchical relationship of sarcasm types in this task.
stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets
Authors: Mengfei Yuan, Zhou Mengyuan, Lianxin Jiang, Yang Mo, Xiaofeng Shi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection

This paper describes the systematic approach applied in “SemEval-2022 Task 6 (iSarcasmEval) : Intended Sarcasm Detection in English and Arabic”. In particular, we illustrate the proposed system in detail for SubTask-A about determining a given text as sarcastic or non-sarcastic in English. We start with the training data from the officially released data and then experiment with different combinations of public datasets to improve the model generalization. Additional experiments conducted on the task demonstrate our strategies are effective in completing the task. Different transformer-based language models, as well as some popular plug-and-play proirs, are mixed into our system to enhance the model’s robustness. Furthermore, statistical and lexical-based text features are mined to improve the accuracy of the sarcasm detection. Our final submission achieves an F1-score for the sarcastic class of 0.6052 on the official test set (the top 1 of the 43 teams in “SubTask-A-English” on the leaderboard).
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification
Authors: Diksha Krishnan, Jerin Mahibha C, Thenmozhi Durairaj
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Author Profiling, Opinion Mining, Sarcasm Detection, Sentiment Analysis

Sarcasm refers to the use of words that have different literal and intended meanings. It represents the usage of words that are opposite of what is literally said, especially in order to insult, mock, criticise or irritate someone. These types of statements may be funny or amusing to others but may hurt or annoy the person towards whom it is intended. Identification of sarcastic phrases from social media posts finds its application in different domains like sentiment analysis, opinion mining, author profiling, and harassment detection. We have proposed a model for the shared task iSarcasmEval - Intended Sarcasm Detection in English and Arabic (CITATION) by SemEval-2022 considering the language English based on ELmo embeddings for Subtasks A and C and TF-IDF vectors and Gaussian Naive bayes classifier for Subtask B. The proposed model resulted in a F1 score 0.2012 for sarcastic texts in Subtask A, macro-F1 score of 0.0387 and 0.2794 for Subtasks B and C respectively.
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling
Authors: Aparna K Ajayan, Krishna Mohanan, Anugraha S, Premjith B, Soman Kp
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Feature Engineering, regression, Sarcasm Detection

This paper describes the submission of the team Amrita_CEN to the shared task on iSarcasm Eval: Intended Sarcasm Detection in English and Arabic at SemEval 2022. We employed machine learning algorithms towards sarcasm detection. Here, we used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, and Decision Tree along with the Random Forest ensemble method. Additionally, feature engineering techniques were applied to deal with the problems of class imbalance during training. Among the models considered, our study shows that the SVM, logistic regression and ensemble model Random Forest exhibited the best performance, which was submitted to the shared task.
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts
Authors: Hamza Alami, Abdessamad Benlahbib, Ahmed Alami
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection

This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.
TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models
Authors: Ramdhanush V, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Language Identification, Sarcasm Detection, Sentiment Analysis

Irony detection in the social media is an upcoming research which places a main role in sentiment analysis and offensive language identification. Sarcasm is one form of irony that is used to provide intended comments against realism. This paper describes a method to detect intended sarcasm in text (SemEval-2022 Task 6). The TECHSSN team used Bidirectional Encoder Representations from Transformers (BERT) models and its variants to classify the text as sarcastic or non-sarcastic in English and Arabic languages. The data is preprocessed and fed to the model for training. The transformer models learn the weights during the training phase from the given dataset and predicts the output class labels for the unseen test data.
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques
Authors: Adrián Moreno Monterde, Laura Vázquez Ramos, Jacinto Mata, Victoria Pachón Álvarez
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sarcasm Detection

Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. This paper describes how the problem proposed in Task 6: Intended Sarcasm Detection in English (Abu Arfa et al. 2022) has been solved. Specifically, we participated in Subtask B: a binary multi-label classification task, where it is necessary to determine whether a tweet belongs to an ironic speech category, if any. Several approaches (classic machine learning and deep learning algorithms) were developed. The final submission consisted of a BERT based model and a macro-F1 score of 0.0699 was obtained.
NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning
Authors: Mostafa Rahgouy, Hamed Babaei Giglou, Taher Rahgooy, Cheryl Seals
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection

The intended sarcasm cannot be understood until the listener observes that the text’s literal meaning violates truthfulness. Consequently, words and meanings play an essential role in specifying sarcasm. Enriched feature extraction techniques were proposed to capture both words and meanings in the contexts. Due to the overlapping features in sarcastic and non-sarcastic texts, a CNN model extracts local features from the combined class-dependent statistical embedding of sarcastic texts with contextualized embedding. Another component BiLSTM extracts long dependencies from combined non-sarcastic statistical and contextualized embeddings. This work combines a classifier that uses the combined high-level features of CNN and BiLSTM for sarcasm detection to produce the final predictions. The experimental analysis presented in this paper shows the effectiveness of the proposed method.
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection
Authors: Emmanuel Osei-Brefo, HuiZhi Liang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection, Sentence

Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature. In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been used with ensemble loss made of cross-entropy loss and negative log-likelihood loss to classify whether a given sentence is in English and Arabic tweets are sarcastic or not. From the results obtained in the experiments, our proposed BERT with ensemble loss achieved superior performance when applied to English and Arabic test datasets. For the validation dataset, our model performed better on the Arabic dataset but failed to outperform the baseline method (made of BERT with only a single loss function) when applied on the English validation set.
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning
Authors: Pablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, Victoria Pachón
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Position, Sarcasm Detection

In this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: Given a text, determine whether it is sarcastic or non-sarcastic. In our approach to complete the task, a comparison of several machine learning and deep learning algorithms using two datasets was conducted. The model which obtained the highest values of F1-score was a BERT-base-cased model. With this one, an F1-score of 0.2451 for the sarcastic class in the evaluation process was achieved. Finally, our team reached the 30th position.
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection
Authors: Abdulrahman Mohamed Kamr, Ensaf Mohamed
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Cultural Vocal Bursts Intensity Prediction, Data Augmentation, Sarcasm Detection

Due to the widespread usage of social media sites and the enormous number of users who utilize irony implicit words in most of their tweets and posts, it has become necessary to detect sarcasm, which strongly influences understanding and analyzing the crowd’s opinions. Detecting sarcasm is difficult due to the nature of sarcastic tweets, which vary based on the topic, region, the user’s attitude, culture, terminologies, and other criteria. In addition to these difficulties, detecting sarcasm in Arabic has its challenges due to its complexities, such as being morphologically rich, having many different dialects, and having low resources.In this research, we present our submission of (iSarcasmEval) sub-task A of the shared task on SemEval 2022. In Sub-task A; we determine whether the tweets are sarcastic or non-sarcastic. We implemented different approaches based on Transformers. First, we fine-tuned the AraBERT, MARABERT, and AraELECTRA. One of the challenges that faced us was that the data was not balanced. Non-sarcastic data is much more than sarcastic. We used data augmentation techniques to balance the two classes, significantly affecting the performance. The performance F1 score of the three models was 87%, 90%, and 91%, respectively. Then we boosted the three models by developing an ensemble model based on hard voting. The final performance F1 Score was 93%.
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques
Authors: Aya Lotfy, Marwan Torki, Nagwa El-Makky
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Natural Language Understanding, Sarcasm Detection

Sarcasm detection is an important task in Natural Language Understanding. Sarcasm is a form of verbal irony that occurs when there is a discrepancy between the literal and intended meanings of an expression. In this paper, we use the tweets of the Arabic dataset provided by SemEval-2022 task 6 to train deep learning classifiers to solve the sub-tasks A and C associated with the dataset. Sub-task A is to determine if the tweet is sarcastic or not. For sub-task C, given a sarcastic text and its non-sarcastic rephrase, i.e. two texts that convey the same meaning, determine which is the sarcastic one. In our solution, we utilize fine-tuned MARBERT (Abdul-Mageed et al., 2021) model with an added single linear layer on top for classification. The proposed solution achieved 0.5076 F1-sarcastic in Arabic sub-task A, accuracy of 0.7450 and F-score of 0.7442 in Arabic sub-task C. We achieved the 2^{nd} and the 9^{th} places for Arabic sub-tasks A and C respectively.
reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets
Authors: Reem Abdel-Salam
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection

This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34& 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72% and 80% accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings.
niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ?
Authors: Nikhil Singh
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Language Modeling, Language Modelling

This paper presents the 10th and 11th place system for Subtask A -English and Subtask A Arabic respectively of the SemEval 2022 -Task 6. The purpose of the Subtask A was to classify a given text sequence into sarcastic and nonsarcastic. We also breifly cover our method for Subtask B which performed subpar when compared with most of the submissions on the official leaderboard . All of the developed solutions used a transformers based language model for encoding the text sequences with necessary changes of the pretrained weights and classifier according to the language and subtask at hand .
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm
Authors: Rishik Lad, Weicheng Ma, Soroush Vosoughi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation, Sarcasm Detection

This paper introduces the result of Team Dartmouth’s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets. This detection was framed as a classification problem, and our contributions are threefold: we developed an English binary classifier system with RoBERTa, an Arabic binary classifier with XLM-RoBERTa, and an English multilabel classifier with BERT. Preprocessing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five subtasks.
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models
Authors: Samantha Huang, Ethan Chi, Nathan Chi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection, Sentiment Analysis

A robust comprehension of sarcasm detection iscritical for creating artificial systems that can ef-fectively perform sentiment analysis in writtentext. In this work, we investigate AI approachesto identifying whether a text is sarcastic or notas part of SemEval-2022 Task 6. We focus oncreating systems for Task A, where we experi-ment with lightweight statistical classificationapproaches trained on both GloVe features andmanually-selected features. Additionally, weinvestigate fine-tuning the transformer modelBERT. Our final system for Task A is an Ex-treme Gradient Boosting Classifier trained onmanually-engineered features. Our final sys-tem achieved an F1-score of 0.2403 on SubtaskA and was ranked 32 of 43.
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation
Authors: Mosab Shaheen, Shubham Nigam
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation, Feature Engineering, Sarcasm Detection, Word Embeddings

The paper describes our submission to SemEval-2022 Task 6 on sarcasm detection and its five subtasks for English and Arabic. Sarcasm conveys a meaning which contradicts the literal meaning, and it is mainly found on social networks. It has a significant role in understanding the intention of the user. For detecting sarcasm, we used deep learning techniques based on transformers due to its success in the field of Natural Language Processing (NLP) without the need for feature engineering. The datasets were taken from tweets. We created new datasets by augmenting with external data or by using word embeddings and repetition of instances. Experiments were done on the datasets with different types of preprocessing because it is crucial in this task. The rank of our team was consistent across four subtasks (fourth rank in three subtasks and sixth rank in one subtask); whereas other teams might be in the top ranks for some subtasks but rank drastically less in other subtasks. This implies the robustness and stability of the models and the techniques we used.
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
Authors: Tanuj Shekhawat, Manoj Kumar, Udaybhan Rathore, Aditya Joshi, Jasabanta Patro
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, we got 19th rank out of 43 teams for sub-task A, 8th rank out of 22 teams for sub-task B, and 13th rank out of 16 teams for sub-task C. Apart from the submitted results and models, we also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data. All of our code and links to additional resources are present in GitHub for reproducibility.
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection
Authors: Patrick Hantsch, Nadav Chkroun
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sarcasm Detection, Sentence, Sentiment Analysis

We investigated the influence of contradictory connotations of words or phrases occurring in sarcastic statements, causing those statements to convey the opposite of their literal meaning. Our approach was to perform a sentiment analysis in order to capture potential opposite sentiments within one sentence and use its results as additional information for a further classifier extracting general text features, testing this for a Convolutional Neural Network, as well as for a Support Vector Machine classifier, respectively.We found that a more complex and sophisticated implementation of the sentiment analysis than just classifying the sentences as positive or negative is necessary, since our implementation showed a worse performance in both approaches than the respective classifier without using any sentiment analysis.
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection
Authors: Jason Angel, Segun Aroyehun, Alexander Gelbukh
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Language Modeling, Language Modelling, Sarcasm Detection

We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022. Specifically we take part in Subtask A for the English language. The task aims to determine whether a text from social media (a tweet) is sarcastic or not. We model the problem using knowledge sources, a pre-trained language model on sentiment/emotion data and a dataset focused on intended sarcasm. Our submission ranked third place among 43 teams. In addition, we show a brief error analysis of our best model to investigate challenging examples for detecting sarcasm.
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic
Authors: Guangmin Zheng, Jin Wang, Xuejie Zhang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Classification, Sarcasm Detection, Sentence, Sentence-Pair Classification

In this paper, we (a YNU-HPCC team) describe the system we built in the SemEval-2022 competition. As participants in Task 6 (titled “iSarcasmEval: Intended Sarcasm Detection In English and Arabic”), we implement the sentiment system for all three subtasks in English and Arabic. All subtasks involve the detection of sarcasm (binary and multilabel classification) and the determination of the sarcastic text location (sentence pair classification). Our system primarily applies the sequence classification model of a bidirectional encoder representation from a transformer (BERT). The BERT is used to extract sentence information from both directions for downstream classification tasks. A single basic model is used for single-sentence and sentence-pair binary classification tasks. For the multilabel task, the Label-Powerset method and binary cross-entropy loss function with weights are used. Our system exhibits competitive performance, obtaining 12/43 (21/32), 11/22, and 3/16 (8/13) rankings in the three official rankings for English (Arabic).