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inproceedings | deng-etal-2022-beike | {BEIKE} {NLP} at {S}em{E}val-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.41/ | Deng, Yong and Dou, Chenxiao and Chen, Liangyu and Miao, Deqiang and Sun, Xianghui and Ma, Baochang and Li, Xiangang | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 319--323 | PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media. Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team`s solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre2trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard. | null | null | 10.18653/v1/2022.semeval-1.41 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,954 |
inproceedings | ramponi-leonardelli-2022-dh | {DH}-{FBK} at {S}em{E}val-2022 Task 4: Leveraging Annotators' Disagreement and Multiple Data Views for Patronizing Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.42/ | Ramponi, Alan and Leonardelli, Elisa | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 324--334 | The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities. Due to its subjective and subtle nature, PCL detection is an open and challenging problem, both for computational methods and human annotators. In this paper we describe the systems submitted by the DH-FBK team to SemEval-2022 Task 4, aiming at detecting PCL towards vulnerable communities in English media texts. Motivated by the subjectivity of human interpretation, we propose to leverage annotators' uncertainty and disagreement to better capture the shades of PCL in a multi-task, multi-view learning framework. Our approach achieves competitive results, largely outperforming baselines and ranking on the top-left side of the leaderboard on both PCL identification and classification. Noticeably, our approach does not rely on any external data or model ensemble, making it a viable and attractive solution for real-world use. | null | null | 10.18653/v1/2022.semeval-1.42 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,955 |
inproceedings | hu-etal-2022-pali | {PALI}-{NLP} at {S}em{E}val-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.43/ | Hu, Dou and Mengyuan, Zhou and Du, Xiyang and Yuan, Mengfei and Zhi, Jin and Jiang, Lianxin and Yang, Mo and Shi, Xiaofeng | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 335--343 | Patronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a novel Transformer-based model and its ensembles to accurately understand such language context for PCL detection. To facilitate comprehension of the subtle and subjective nature of PCL, two fine-tuning strategies are applied to capture discriminative features from diverse linguistic behaviour and categorical distribution. The system achieves remarkable results on the official ranking, including 1st in Subtask 1 and 5th in Subtask 2. Extensive experiments on the task demonstrate the effectiveness of our system and its strategies. | null | null | 10.18653/v1/2022.semeval-1.43 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,956 |
inproceedings | rao-2022-asrtrans | {ASR}trans at {S}em{E}val-2022 Task 4: Ensemble of Tuned Transformer-based Models for {PCL} Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.44/ | Rao, Ailneni Rakshitha | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 344--351 | Patronizing behavior is a subtle form of bullying and when directed towards vulnerable communities, it can arise inequalities. This paper describes our system for Task 4 of SemEval-2022: Patronizing and Condescending Language Detection (PCL). We participated in both the sub-tasks and conducted extensive experiments to analyze the effects of data augmentation and loss functions used, to tackle the problem of class imbalance. We explore whether large transformer-based models can capture the intricacies associated with PCL detection. Our solution consists of an ensemble of the RoBERTa model which is further trained on external data and other language models such as XLNeT, Ernie-2.0, and BERT. We also present the results of several problem transformation techniques such as Classifier Chains, Label Powerset, and Binary relevance for multi-label classification. | null | null | 10.18653/v1/2022.semeval-1.44 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,957 |
inproceedings | agrawal-mamidi-2022-lastresort | {L}ast{R}esort at {S}em{E}val-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models Ensembles | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.45/ | Agrawal, Samyak and Mamidi, Radhika | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 352--356 | This paper presents our solutions systems for Task4 at SemEval2022: Patronizing and Condescending Language Detection. This shared task contains two sub-tasks. The first sub-task is a binary classification task whose goal is to predict whether a given paragraph contains any form of patronising or condescending language(PCL). For the second sub-task, given a paragraph, we have to find which PCL categories express the condescension. Here we have a total of 7 overlapping sub-categories for PCL. Our proposed solution uses BERT based ensembled models with hard voting and techniques applied to take care of class imbalances. Our paper describes the system architecture of the submitted solution and other experiments that we conducted. | null | null | 10.18653/v1/2022.semeval-1.45 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,958 |
inproceedings | herrmann-krebs-2022-felix | Felix{\&}Julia at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.46/ | Herrmann, Felix and Krebs, Julia | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 357--362 | This paper describes the authors' submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Subtask 1 includes a binary classification task (PCL or not PCL). Subtask 2 is a multi label classification task where the system identifies different categories of PCL. The authors of this paper submitted two different models: one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model. | null | null | 10.18653/v1/2022.semeval-1.46 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,959 |
inproceedings | meyer-etal-2022-ms | {MS}@{IW} at {S}em{E}val-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated Data | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.47/ | Meyer, Selina and Schmidhuber, Maximilian and Kruschwitz, Udo | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 363--368 | In this description paper we outline the system architecture submitted to Task 4, Subtask 1 at SemEval-2022. We leverage the generative power of state of the art generative pretrained transformer models to increase training set size and remedy class imbalance issues. Our best submitted system is trained on a synthetically enhanced dataset with 10.3 times as many positive samples as the original dataset and reaches an F1 score of 50.62{\%}, which is 10 percentage points higher than our initial system trained on an undersampled version of the original dataset. We explore possible reasons for the comparably low score in the overall task ranking and report on experiments conducted during the post-evaluation phase. | null | null | 10.18653/v1/2022.semeval-1.47 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,960 |
inproceedings | singh-2022-team | Team {LEGO} at {S}em{E}val-2022 Task 4: Machine Learning Methods for {PCL} Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.48/ | Singh, Abhishek | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 369--373 | In this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection. Weapproach this problem as a traditional text classification problem with machine learning (ML)methods. We experiment and investigate theuse of various ML algorithms for detecting PCL in news articles. Our best methodology achieves an F1- Score of 0.39 for subtask1 witha rank of 63 out of 80, and F1-score of 0.082for subtask2 with a rank of 41 out of 48 on the blind dataset provided in the shared task. | null | null | 10.18653/v1/2022.semeval-1.48 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,961 |
inproceedings | yang-etal-2022-rnre | {RNRE}-{NLP} at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.49/ | Yang, Rylan and Chi, Ethan and Chi, Nathan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 374--378 | An understanding of patronizing and condescending language detection is an important part of identifying and addressing discrimination and prejudice in various forms of communication. In this paper, we investigate several methods for detecting patronizing and condescending language in short statements as part of SemEval-2022 Task 4. For Task 1a, we investigate applying both lightweight (tree-based and linear) machine learning classification models and fine-tuned pre-trained large language models. Our final system achieves an F1-score of 0.4321, recall-score of 0.5016, and a precision-score of 0.3795 (ranked 53 / 78) on Task 1a. | null | null | 10.18653/v1/2022.semeval-1.49 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,962 |
inproceedings | zhao-rios-2022-utsa | {UTSA} {NLP} at {S}em{E}val-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.50/ | Zhao, Xingmeng and Rios, Anthony | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 379--386 | The act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those that experience it. Thus, detecting this condescending and patronizing language online can be useful for online moderation systems. Thus, in this manuscript, we describe the system developed by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending Language. Our approach explores the use of several deep learning architectures including RoBERTa, convolutions neural networks, and Bidirectional Long Short-Term Memory Networks. Furthermore, we explore simple and effective methods to create ensembles of neural network models. Overall, we experimented with several ensemble models and found that the a simple combination of five RoBERTa models achieved an F-score of .6441 on the development dataset and .5745 on the final test dataset. Finally, we also performed a comprehensive error analysis to better understand the limitations of the model and provide ideas for further research. | null | null | 10.18653/v1/2022.semeval-1.50 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,963 |
inproceedings | edalat-etal-2022-aliedalat | {A}li{E}dalat at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, {BERT}+{B}i{GRU}, and Ensemble Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.51/ | Edalat, Ali and Yaghoobzadeh, Yadollah and Bahrak, Behnam | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 387--393 | This paper presents the AliEdalat team`s methodology and results in SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. This task aims to detect the presence of PCL and PCL categories in text in order to prevent further discrimination against vulnerable communities. We use an ensemble of three basic models to detect the presence of PCL: fine-tuned bigbird, fine-tuned mpnet, and BERT+BiGRU. The ensemble model performs worse than the baseline due to overfitting and achieves an F1-score of 0.3031. We offer another solution to resolve the submitted model`s problem. We consider the different categories of PCL separately. To detect each category of PCL, we act like a PCL detector. Instead of BERT+BiGRU, we use fine-tuned roberta in the models. In PCL category detection, our model outperforms the baseline model and achieves an F1-score of 0.2531. We also present new models for detecting two categories of PCL that outperform the submitted models. | null | null | 10.18653/v1/2022.semeval-1.51 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,964 |
inproceedings | bhatt-shrivastava-2022-tesla | Tesla at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.52/ | Bhatt, Sahil and Shrivastava, Manish | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 394--399 | This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL or non-PCL, we use a T5 Model fine-tuned on the dataset. For sub-task 2, which is a multi-label classification problem, we use a RoBERTa model fine-tuned on the dataset. Given that the key challenge in this task is classification on an imbalanced dataset, our models rely on an augmented dataset that we generate using paraphrasing. We found that these two models yield the best results out of all the other approaches we tried. | null | null | 10.18653/v1/2022.semeval-1.52 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,965 |
inproceedings | adaikkan-durairaj-2022-ssn | {SSN}{\_}{NLP}{\_}{MLRG} at {S}em{E}val-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.53/ | Adaikkan, Kalaivani and Durairaj, Thenmozhi | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 400--404 | In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities. The shared task consists of two subtasks: Patronizing and Condescending language detection (Subtask A) which is the binary task classification and identifying the PCL categories that express the condescension (Subtask B) which is the multi-label text classification. For PCL language detection, We proposed the ensemble strategies of a system combination of BERT, Roberta, Distilbert, Roberta large, Albert achieved the official results for Subtask A with a macro f1 score of 0.5172 on the test set which is improved by baseline score. For PCL Category identification, We proposed a multi-label classification model to ensemble the various Bert-based models and the official results for Subtask B with a macro f1 score of 0.2117 on the test set which is improved by baseline score. | null | null | 10.18653/v1/2022.semeval-1.53 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,966 |
inproceedings | li-zhou-2022-sapphire | Sapphire at {S}em{E}val-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.54/ | Li, Sihui and Zhou, Xiaobing | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 405--408 | This paper introduces the related work and the results of Team Sapphire`s system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection. We only participated in subtask 1. The task goal is to judge whether a news text contains PCL. This task can be considered as a task of binary classification of news texts. In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it. Capsule networks is adopted to extract features from the encoded vectors. The official evaluation metric for subtask 1 is the F1 score over the positive class. Finally, our system`s submitted prediction results on test set achieved the score of 0.5187. | null | null | 10.18653/v1/2022.semeval-1.54 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,967 |
inproceedings | siino-etal-2022-mcrock | {M}c{R}ock at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel {CNN}, Hybrid {LSTM}, {D}istil{BERT} and {XLN}et | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.55/ | Siino, Marco and Cascia, Marco and Tinnirello, Ilenia | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 409--417 | In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network (CNN) and an Hybrid LSTM. Using the multi-channel CNN we explore the impact of parallel word emebeddings and convolutional layers involving different kernel sizes. With Hybrid LSTM we focus on extracting features in advance, thanks to a convolutional layer followed by two bidirectional LSTM layers. For the second subtask a Transformer BERT-based model (i.e. DistilBERT) and an XLNet-based model are proposed. The multi-channel CNN model is able to reach an F1 score of 0.2928, the Hybrid LSTM modelis able to reach an F1 score of 0.2815, the DistilBERT-based one an average F1 of 0.2165 and the XLNet an average F1 of 0.2296. In this paper, in addition to system descriptions, we also provide further analysis of the results, highlighting strengths and limitations. We make all the code publicly available and reusable on GitHub. | null | null | 10.18653/v1/2022.semeval-1.55 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,968 |
inproceedings | dass-vattam-etal-2022-team | Team {S}tanford {ACML}ab at {S}em{E}val 2022 Task 4: Textual Analysis of {PCL} Using Contextual Word Embeddings | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.56/ | Dass-Vattam, Upamanyu and Wallace, Spencer and Sikand, Rohan and Witzel, Zach and Tang, Jillian | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 418--420 | We propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL). We finetuned a pre-trained BERT model to detect whether or not a paragraph contained PCL (Subtask 1), and furthermore finetuned another pre-trained BERT model to identify the linguistic techniques used to convey the PCL (Subtask 2). Results show that this approach is viable for binary classification of PCL, but breaks when attempting to identify the PCL techniques. Our system placed 32/79 for subtask 1, and 40/49 for subtask 2. | null | null | 10.18653/v1/2022.semeval-1.56 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,969 |
inproceedings | tandon-chatterjee-2022-team | Team {LRL}{\_}{NC} at {S}em{E}val-2022 Task 4: Binary and Multi-label Classification of {PCL} using Fine-tuned Transformer-based Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.57/ | Tandon, Kushagri and Chatterjee, Niladri | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 421--431 | Patronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented by the task emerges from the subtleties of PCL and various data dependent constraints. Hence, developing techniques to detect PCL in text, before it is propagated is vital. The aim of this paper is twofold, a) to present systems that can be used to classify a text as containing PCL or not, and b) to present systems that assign the different categories of PCL present in text. The proposed systems are primarily rooted in transformer-based pre-trained language models. Among the models submitted for Subtask 1, the best F1-Score of 0.5436 was achieved by a deep learning based ensemble model. This system secured the rank 29 in the official task ranking. For Subtask 2, the best macro-average F1-Score of 0.339 was achieved by an ensemble model combining transformer-based neural architecture with gradient boosting label-balanced classifiers. This system secured the rank 21 in the official task ranking. Among subsequently carried out experiments a variation in architecture of a system for Subtask 2 achieved a macro-average F1-Score of 0.3527. | null | null | 10.18653/v1/2022.semeval-1.57 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,970 |
inproceedings | lu-etal-2022-guts | {GUTS} at {S}em{E}val-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.58/ | Lu, Junyu and Zhang, Hao and Zhang, Tongyue and Wang, Hongbo and Zhu, Haohao and Xu, Bo and Lin, Hongfei | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 432--437 | Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is not aware of the language`s toxicity. In this paper, we present our method for the SemEval-2022 Task4 titled {\textquotedblleft}Patronizing and Condescending Language Detection{\textquotedblright}. In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture. For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method. In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively. In addition, we explore the relationship between PCL and emotional polarity and intensity it contains. | null | null | 10.18653/v1/2022.semeval-1.58 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,971 |
inproceedings | liu-etal-2022-hitmi | {HITMI}{\&}{T} at {S}em{E}val-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On {PCL} Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.59/ | Liu, Zihang and He, Yancheng and Zhuang, Feiqing and Xu, Bing | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 438--444 | This paper describes the system for the Semeval-2022 Task4 {\textquotedblright}Patronizing and Condescending Language Detection{\textquotedblright}.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards others or depicts them in a compassionate way. The task contains two parts. The first one is to identify whether the sentence is PCL, and the second one is to categorize PCL. Through experimental verification, the Roberta-based model will be used in our system. Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied. We give a clear ablation experiment and give the effect of each method on the final result. Our project ranked 11th out of 79 teams participating in subtask 1 and 6th out of 49 teams participating in subtask 2. | null | null | 10.18653/v1/2022.semeval-1.59 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,972 |
inproceedings | koleczek-etal-2022-umass | {UM}ass {PCL} at {S}em{E}val-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.60/ | Koleczek, David and Scarlatos, Alexander and Pereira, Preshma Linet and Karkare, Siddha Makarand | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 445--453 | Patronizing and condescending language (PCL) is everywhere, but rarely is the focus on its use by media towards vulnerable communities. Accurately detecting PCL of this form is a difficult task due to limited labeled data and how subtle it can be. In this paper, we describe our system for detecting such language which was submitted to SemEval 2022 Task 4: Patronizing and Condescending Language Detection. Our approach uses an ensemble of pre-trained language models, data augmentation, and optimizing the threshold for detection. Experimental results on the evaluation dataset released by the competition hosts show that our work is reliably able to detect PCL, achieving an F1 score of 55.47{\%} on the binary classification task and a macro F1 score of 36.25{\%} on the fine-grained, multi-label detection task. | null | null | 10.18653/v1/2022.semeval-1.60 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,973 |
inproceedings | bai-etal-2022-ynu | {YNU}-{HPCC} at {S}em{E}val-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.61/ | Bai, Wenqiang and Wang, Jin and Zhang, Xuejie | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 454--458 | This paper describes a system built in the SemEval-2022 competition. As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks in English. Both subtasks involve determining emotions; subtask 1 requires us to determine whether the text belongs to the PCL category (single-label classification), and subtask 2 requires us to determine to which PCL category the text belongs (multi-label classification). Our system is based on the bidirectional encoder representations from transformers (BERT) model. For the single-label classification, our system applies a BertForSequenceClassification model to classify the input text. For the multi-label classification, we use the fine-tuned BERT model to extract the sentiment score of the text and a fully connected layer to classify the text into the PCL categories. Our system achieved relatively good results on the competition`s official leaderboard. | null | null | 10.18653/v1/2022.semeval-1.61 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,974 |
inproceedings | vazquez-ramos-etal-2022-i2c | {I}2{C} at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.62/ | V{\'a}zquez Ramos, Laura and Moreno Monterde, Adri{\'a}n and Pach{\'o}n, Victoria and Mata, Jacinto | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 459--463 | Patronizing and Condescending Language is an ever-present problem in our day-to-day lives. There has been a rise in patronizing language on social media platforms manifesting itself in various forms. This paper presents two performing deep learning algorithms and results for the {\textquotedblleft}Task 4: Patronizing and Condescending Language Detection.{\textquotedblright} of SemEval 2022. The task incorporates an English dataset containing sentences from social media from around the world. The paper focuses on data augmentation to boost results on various deep learning methods as BERT and LSTM Neural Network. | null | null | 10.18653/v1/2022.semeval-1.62 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,975 |
inproceedings | suri-2022-pickle | {P}i{C}k{L}e at {S}em{E}val-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.63/ | Suri, Manan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 464--472 | This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection. Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities that portrays them in a pitiful way and is reflective of a sense of superiority. Task 4 involved binary classification (Subtask 1) and multi-label classification (Subtask 2) of Patronizing and Condescending Language (PCL). For our system, we experimented with fine-tuning different transformer-based pre-trained models including BERT, DistilBERT, RoBERTa and ALBERT. Further, we have used token separated metadata in order to improve our model by helping it contextualize different communities with respect to PCL. We faced the challenge of class imbalance, which we solved by experimenting with different class weighting schemes. Our models were effective in both subtasks, with the best performance coming out of models with Effective Number of Samples (ENS) class weighting and token separated metadata in both subtasks. For subtask 1 and subtask 2, our best models were finetuned BERT and RoBERTa models respectively. | null | null | 10.18653/v1/2022.semeval-1.63 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,976 |
inproceedings | adewumi-etal-2022-ml | {ML}{\_}{LTU} at {S}em{E}val-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.64/ | Adewumi, Tosin and Alkhaled, Lama and Mokayed, Hamam and Liwicki, Foteini and Liwicki, Marcus | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 473--478 | This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes {\&} struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set. | null | null | 10.18653/v1/2022.semeval-1.64 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,977 |
inproceedings | xu-2022-xu | Xu at {S}em{E}val-2022 Task 4: Pre-{BERT} Neural Network Methods vs Post-{BERT} {R}o{BERT}a Approach for Patronizing and Condescending Language Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.65/ | Xu, Jinghua | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 479--484 | This paper describes my participation in the SemEval-2022 Task 4: Patronizing and Condescending Language Detection. I participate in both subtasks: Patronizing and Condescending Language (PCL) Identification and Patronizing and Condescending Language Categorization, with the main focus put on subtask 1. The experiments compare pre-BERT neural network (NN) based systems against post-BERT pretrained language model RoBERTa. This research finds NN-based systems in the experiments perform worse on the task compared to the pretrained language models. The top-performing RoBERTa system is ranked 26 out of 78 teams (F1-score: 54.64) in subtask 1, and 23 out of 49 teams (F1-score: 30.03) in subtask 2. | null | null | 10.18653/v1/2022.semeval-1.65 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,978 |
inproceedings | mosquera-2022-amsqr | Amsqr at {S}em{E}val-2022 Task 4: Towards {A}uto{NLP} via Meta-Learning and Adversarial Data Augmentation for {PCL} Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.66/ | Mosquera, Alejandro | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 485--489 | This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario. The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization. A submission from this system was evaluated as part of the first sub-task of SemEval 2022 - Task 4 and achieved an F1 score of 0.57{\%}, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers. | null | null | 10.18653/v1/2022.semeval-1.66 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,979 |
inproceedings | bestgen-2022-satlab | {SATL}ab at {S}em{E}val-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.67/ | Bestgen, Yves | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 490--495 | A logistic regression model only fed with character and word n-grams is proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language Detection (PCL). It obtained an average level of performance, well above the performance of a system that tries to guess without using any knowledge about the task, but much lower than the best teams. To facilitate the interpretation of the performance scores, the F1 measure, the best level of performance of a system that tries to guess without using any knowledge is calculated and used to correct the F1 scores in the manner of a Kappa. As the proposed model is very similar to the one that performed well on a task requiring to automatically identify hate speech and offensive content, this paper confirms the difficulty of PCL detection. | null | null | 10.18653/v1/2022.semeval-1.67 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,980 |
inproceedings | chhillar-2022-taygete | Taygete at {S}em{E}val-2022 Task 4: {R}o{BERT}a based models for detecting Patronising and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.68/ | Chhillar, Jayant | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 496--502 | This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763. | null | null | 10.18653/v1/2022.semeval-1.68 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,981 |
inproceedings | saeedi-etal-2022-cs | {CS}/{NLP} at {S}em{E}val-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with {R}o{BERT}a and {GPT}3 | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.69/ | Saeedi, Daniel and Saeedi, Sirwe and Panahi, Aliakbar and C.M. Fong, Alvis | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 503--508 | This paper presents a combination of data augmentation methods to boost the performance of state-of-the-art transformer-based language models for Patronizing and Condescending Language (PCL) detection and multi-label PCL classification tasks. These tasks are inherently different from sentiment analysis because positive/negative hidden attitudes in the context will not necessarily be considered positive/negative for PCL tasks. The oblation study observes that the imbalance degree of PCL dataset is in the extreme range. This paper presents a modified version of the sentence paraphrasing deep learning model (PEGASUS) to tackle the limitation of maximum sequence length. The proposed algorithm has no specific maximum input length to paraphrase sequences. Our augmented underrepresented class of annotated data achieved competitive results among top-16 SemEval-2022 participants. This paper`s approaches rely on fine-tuning pretrained RoBERTa and GPT3 models such as Davinci and Curie engines with an extra-enriched PCL dataset. Furthermore, we discuss Few-Shot learning technique to overcome the limitation of low-resource NLP problems. | null | null | 10.18653/v1/2022.semeval-1.69 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,982 |
inproceedings | dumitrascu-etal-2022-university | University of Bucharest Team at {S}emeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.70/ | Dumitrascu, Tudor and G{\^i}nga, Raluca-Andreea and Dobre, Bogdan and Radu Silviu Sielecki, Bogdan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 509--514 | This paper details our implementations for finding Patronizing and Condescending Language in texts, as part of the SemEval Workshop Task 4. We have used a variety of methods from simple machine learning algorithms applied on bag of words, all the way to BERT models, in order to solve the binary classification and the multi-label multi-class classification. | null | null | 10.18653/v1/2022.semeval-1.70 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,983 |
inproceedings | george-etal-2022-amrita | {A}mrita{\_}{CEN} at {S}em{E}val-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.71/ | George, Bichu and S, Adarsh and Prajapati, Nishitkumar and B, Premjith and Kp, Soman | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 515--518 | This paper narrates the work of the team Amrita{\_}CEN for the shared task on Patronizing and Condescending Language Detection at SemEval 2022. We implemented machine learning algorithms such as Support Vector Machine (SVV), Logistic regression, Naive Bayes, XG Boost and Random Forest for modelling the tasks. At the same time, we also applied a feature engineering method to solve the class imbalance problem with respect to training data. Among all the models, the logistic regression model outperformed all other models and we have submitted results based upon the same. | null | null | 10.18653/v1/2022.semeval-1.71 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,984 |
inproceedings | hacohen-kerner-etal-2022-jct | {JCT} at {S}em{E}val-2022 Task 4-A: Patronism Detection in Posts Written in {E}nglish using Preprocessing Methods and various Machine Leaerning Methods | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.72/ | HaCohen-Kerner, Yaakov and Meyrowitsch, Ilan and Fchima, Matan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 519--524 | In this paper, we describe our submissions to SemEval-2022 subtask 4-A - {\textquotedblleft}Patronizing and Condescending Language Detection: Binary Classification{\textquotedblright}. We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced dataset. The f1-scores on the oversampled imbalanced training dataset were higher the results on the original training dataset. | null | null | 10.18653/v1/2022.semeval-1.72 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,985 |
inproceedings | klemen-robnik-sikonja-2022-ulfri | {ULFRI} at {S}em{E}val-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.73/ | Klemen, Matej and Robnik-{\v{S}}ikonja, Marko | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 525--532 | We describe the ULFRI system used in the Subtask 1 of SemEval-2022 Task 4 Patronizing and condescending language detection. Our models are based on the RoBERTa model, modified in two ways: (1) by injecting additional knowledge (coreferences, named entities, dependency relations, and sentiment) and (2) by leveraging the task uncertainty by using soft labels, Monte Carlo dropout, and threshold optimization. We find that the injection of additional knowledge is not helpful but the uncertainty management mechanisms lead to small but consistent improvements. Our final system based on these findings achieves F1 = 0.575 in the online evaluation, ranking 19th out of 78 systems. | null | null | 10.18653/v1/2022.semeval-1.73 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,986 |
inproceedings | fersini-etal-2022-semeval | {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.74/ | Fersini, Elisabetta and Gasparini, Francesca and Rizzi, Giulia and Saibene, Aurora and Chulvi, Berta and Rosso, Paolo and Lees, Alyssa and Sorensen, Jeffrey | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 533--549 | The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives. | null | null | 10.18653/v1/2022.semeval-1.74 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,987 |
inproceedings | mahadevan-etal-2022-transformers | Transformers at {S}em{E}val-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.75/ | Mahadevan, Shankar and Benhur, Sean and Nayak, Roshan and Subramanian, Malliga and Shanmugavadivel, Kogilavani and Sivanraju, Kanchana and Chakravarthi, Bharathi Raja | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 550--554 | Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women`s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal. In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins. | null | null | 10.18653/v1/2022.semeval-1.75 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,988 |
inproceedings | zhi-etal-2022-paic | {PAIC} at {S}em{E}val-2022 Task 5: Multi-Modal Misogynous Detection in {MEMES} with Multi-Task Learning And Multi-model Fusion | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.76/ | Zhi, Jin and Mengyuan, Zhou and Yuan, Mengfei and Hu, Dou and Du, Xiyang and Jiang, Lianxin and Mo, Yang and Shi, XiaoFeng | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 555--562 | This paper describes our system used in the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI). Multimedia automatic misogyny recognition consists of the identification of misogynous memes, taking advantage of both text and images as sources of information. The task will be organized around two main subtasks: Task A is a binary classification task, which should be identified either as misogynous or not misogynous. Task B is a multi-label classification task, in which the types of misogyny should be identified in potential overlapping categories, such as stereotype, shaming, objectification, and violence. In this paper, we proposed a system based on multi-task learning for multi-modal misogynous detection in memes. Our system combined image features with text features to train a multi-label classification. The prediction results were obtained by the simple weighted average method of the results with different fusion models, and the results of Task A were corrected by Task B. Our system achieves a test accuracy of 0.755 on Task A (ranking 3rd on the final leaderboard) and the accuracy of 0.731 on Task B (ranking 1st on the final leaderboard). | null | null | 10.18653/v1/2022.semeval-1.76 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,989 |
inproceedings | zhou-etal-2022-dd-tig | {DD}-{TIG} at {S}em{E}val-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and Classification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.77/ | Zhou, Ziming and Zhao, Han and Dong, Jingjing and Ding, Ning and Liu, Xiaolong and Zhang, Kangli | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 563--570 | This paper describes our submission for task 5 Multimedia Automatic Misogyny Identification (MAMI) at SemEval-2022. The task is designed to detect and classify misogynous memes. To utilize both textual and visual information presented in a meme, we investigate several of the most recent visual language transformer-based multimodal models and choose ERNIE-ViL-Large as our base model. For subtask A, with observations of models' overfitting on unimodal patterns, strategies are proposed to mitigate problems of biased words and template memes. For subtask B, we transform this multi-label problem into a multi-class one and experiment with oversampling and complementary techniques. Our approach places 2nd for subtask A and 5th for subtask B in this competition. | null | null | 10.18653/v1/2022.semeval-1.77 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,990 |
inproceedings | sivanaiah-etal-2022-techssn | {T}ech{SSN} at {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.78/ | Sivanaiah, Rajalakshmi and S, Angel and Rajendram, Sakaya Milton and T T, Mirnalinee | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 571--574 | Research is progressing in a fast manner in the field of offensive, hate speech, abusive and sarcastic data. Tackling hate speech against women is urgent and really needed to give respect to the lady of our life. This paper describes the system used for identifying misogynous content using images and text. The system developed by the team TECHSSN uses transformer models to detect the misogynous content from text and Convolutional Neural Network model for image data. Various models like BERT, ALBERT, XLNET and CNN are explored and the combination of ALBERT and CNN as an ensemble model provides better results than the rest. This system was developed for the task 5 of the competition, SemEval 2022. | null | null | 10.18653/v1/2022.semeval-1.78 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,991 |
inproceedings | agrawal-mamidi-2022-lastresort-semeval | {L}ast{R}esort at {S}em{E}val-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.79/ | Agrawal, Samyak and Mamidi, Radhika | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 575--580 | In current times, memes have become one of the most popular mediums to share jokes and information with the masses over the internet. Memes can also be used as tools to spread hatred and target women through degrading content disguised as humour. The task, Multimedia Automatic Misogyny Identification (MAMI), is to detect misogyny in these memes. This task is further divided into two sub-tasks: (A) Misogynous meme identification, where a meme should be categorized either as misogynous or not misogynous and (B) Categorizing these misogynous memes into potential overlapping subcategories. In this paper, we propose models leveraging task-specific pretraining with transfer learning on Visual Linguistic models. Our best performing models scored 0.686 and 0.691 on sub-tasks A and B respectively. | null | null | 10.18653/v1/2022.semeval-1.79 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,992 |
inproceedings | arango-etal-2022-hateu | {H}ate{U} at {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.80/ | Arango, Ayme and Perez-Martin, Jesus and Labrada, Arniel | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 581--584 | Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71{\%} of F-score using a multimodal system based on the CLIP model. | null | null | 10.18653/v1/2022.semeval-1.80 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,993 |
inproceedings | zhang-wang-2022-srcb | {SRCB} at {S}em{E}val-2022 Task 5: Pretraining Based Image to Text Late Sequential Fusion System for Multimodal Misogynous Meme Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.81/ | Zhang, Jing and Wang, Yujin | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 585--596 | Online misogyny meme detection is an image/text multimodal classification task, the complicated relation of image and text challenges the intelligent system`s modality fusion learning capability. In this paper, we investigate the single-stream UNITER and dual-stream CLIP multimodal pretrained models on their capability to handle strong and weakly correlated image/text pairs. The XGBoost classifier with image features extracted by the CLIP model has the highest performance and being robust on domain shift. Based on this, we propose the PBR system, an ensemble system of Pretraining models, Boosting method and Rule-based adjustment, text information is fused into the system using our late sequential fusion scheme. Our system ranks 1st place on both sub-task A and sub-task B of the SemEval-2022 Task 5 Multimedia Automatic Misogyny Identification, with 0.834/0.731 macro F1 scores for sub-task A/B correspondingly. | null | null | 10.18653/v1/2022.semeval-1.81 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,994 |
inproceedings | rao-rao-2022-asrtrans | {ASR}trans at {S}em{E}val-2022 Task 5: Transformer-based Models for Meme Classification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.82/ | Rao, Ailneni Rakshitha and Rao, Arjun | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 597--604 | Women are frequently targeted online with hate speech and misogyny using tweets, memes, and other forms of communication. This paper describes our system for Task 5 of SemEval-2022: Multimedia Automatic Misogyny Identification (MAMI). We participated in both the sub-tasks, where we used transformer-based architecture to combine features of images and text. We explore models with multi-modal pre-training (VisualBERT) and text-based pre-training (MMBT) while drawing comparative results. We also show how additional training with task-related external data can improve the model performance. We achieved sizable improvements over baseline models and the official evaluation ranked our system $3^{rd}$ out of 83 teams on the binary classification task (Sub-task A) with an F1 score of 0.761, and $7^{th}$ out of 48 teams on the multi-label classification task (Sub-task B) with an F1 score of 0.705. | null | null | 10.18653/v1/2022.semeval-1.82 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,995 |
inproceedings | roman-rangel-etal-2022-uaem | {UAEM}-{ITAM} at {S}em{E}val-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.83/ | Roman-Rangel, Edgar and Fuentes-Pacheco, Jorge and Hermosillo Valadez, Jorge | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 605--609 | In the context of the Multimedia Automatic Misogyny Identification (MAMI) competition 2022, we developed a framework for extracting lexical-semantic features from text and combine them with semantic descriptions of images, together with image content representation. We enriched the text modality description by incorporating word representations for each object present within the images. Images and text are then described at two levels of detail, globally and locally, using standard dimensionality reduction techniques for images in order to obtain 4 embeddings for each meme. These embeddings are finally concatenated and passed to a classifier. Our results overcome the baseline by 4{\%}, falling behind the best performance by 12{\%} for Sub-task B. | null | null | 10.18653/v1/2022.semeval-1.83 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,996 |
inproceedings | ravagli-vaiani-2022-jrlv | {JRLV} at {S}em{E}val-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.84/ | Ravagli, Jason and Vaiani, Lorenzo | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 610--617 | Gender discrimination is a serious and widespread problem on social media and online in general. Besides offensive messages, memes are one of the main means of dissemination for such content. With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics. In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task. Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances. Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images. | null | null | 10.18653/v1/2022.semeval-1.84 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,997 |
inproceedings | paraschiv-etal-2022-upb | {UPB} at {S}em{E}val-2022 Task 5: Enhancing {UNITER} with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.85/ | Paraschiv, Andrei and Dascalu, Mihai and Cercel, Dumitru-Clementin | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 618--625 | In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4{\%} in Sub-task A and 67.3{\%} for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub. | null | null | 10.18653/v1/2022.semeval-1.85 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,998 |
inproceedings | yu-etal-2022-rubcsg | {R}ub{CSG} at {S}em{E}val-2022 Task 5: Ensemble learning for identifying misogynous {MEME}s | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.86/ | Yu, Wentao and Boenninghoff, Benedikt and R{\"ohrig, Jonas and Kolossa, Dorothea | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 626--635 | This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B. | null | null | 10.18653/v1/2022.semeval-1.86 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 22,999 |
inproceedings | chen-chou-2022-rit | {RIT} Boston at {S}em{E}val-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from {CLIP} Model and Data-centric {AI} Principle | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.87/ | Chen, Lei and Chou, Hou Wei | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 636--641 | Detecting MEME images to be misogynous or not is an application useful on curbing online hateful information against women. In the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) challenge, we designed a system using two simple but effective principles. First, we leverage on recently emerging Transformer models pre-trained (mostly in a self-supervised learning way) on massive data sets to obtain very effective visual (V) and language (L) features. In particular, we used the CLIP model provided by OpenAI to obtain coherent V and L features and then simply used a logistic regression model to make binary predictions. Second, we emphasized more on data rather than tweaking models by following the data-centric AI principle. These principles were proven to be useful and our final macro-F1 is 0.778 for the MAMI task A and ranked the third place among participant teams. | null | null | 10.18653/v1/2022.semeval-1.87 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,000 |
inproceedings | maheshwari-nangi-2022-teamotter | {T}eam{O}tter at {S}em{E}val-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.88/ | Maheshwari, Paridhi and Nangi, Sharmila Reddy | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 642--647 | We describe our system for the SemEval 2022 task on detecting misogynous content in memes. This is a pressing problem and we explore various methods ranging from traditional machine learning to deep learning models such as multimodal transformers. We propose a multimodal BERT architecture that uses information from both image and text. We further incorporate common world knowledge from pretrained CLIP and Urban dictionary. We also provide qualitative analysis to support out model. Our best performing model achieves an F1 score of 0.679 on Task A (Rank 5) and 0.680 on Task B (Rank 13) of the hidden test set. Our code is available at \url{https://github.com/paridhimaheshwari2708/MAMI}. | null | null | 10.18653/v1/2022.semeval-1.88 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,001 |
inproceedings | tao-kim-2022-taochen | taochen at {S}em{E}val-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.89/ | Tao, Chen and Kim, Jung-jae | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 648--653 | We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work. | null | null | 10.18653/v1/2022.semeval-1.89 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,002 |
inproceedings | attanasio-etal-2022-milanlp | {M}ila{NLP} at {S}em{E}val-2022 Task 5: Using Perceiver {IO} for Detecting Misogynous Memes with Text and Image Modalities | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.90/ | Attanasio, Giuseppe and Nozza, Debora and Bianchi, Federico | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 654--662 | In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge. We use Perceiver IO as a multimodal late fusion over unimodal streams to address both sub-tasks A and B. We build unimodal embeddings using Vision Transformer (image) and RoBERTa (text transcript). We enrich the input representation using face and demographic recognition, image captioning, and detection of adult content and web entities. To the best of our knowledge, this work is the first to use Perceiver IO combining text and image modalities. The proposed approach outperforms unimodal and multimodal baselines. | null | null | 10.18653/v1/2022.semeval-1.90 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,003 |
inproceedings | muti-etal-2022-unibo | {U}ni{BO} at {S}em{E}val-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.91/ | Muti, Arianna and Korre, Katerina and Barr{\'o}n-Cede{\~n}o, Alberto | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 663--672 | We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F$_1$=0.727 in Task A (4th position out of 69 participants)and weighted F$_1$=0.710 in Task B (4th position out of 42 participants). | null | null | 10.18653/v1/2022.semeval-1.91 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,004 |
inproceedings | raha-etal-2022-iiith | {IIITH} at {S}em{E}val-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.92/ | Raha, Tathagata and Joshi, Sagar and Varma, Vasudeva | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 673--678 | This paper provides a comparison of different deep learning methods for identifying misogynous memes for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. In this task, we experiment with architectures in the identification of misogynous content in memes by making use of text and image-based information. The different deep learning methods compared in this paper are: (i) unimodal image or text models (ii) fusion of unimodal models (iii) multimodal transformers models and (iv) transformers further pretrained on a multimodal task. From our experiments, we found pretrained multimodal transformer architectures to strongly outperform the models involving the fusion of representation from both the modalities. | null | null | 10.18653/v1/2022.semeval-1.92 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,005 |
inproceedings | mahran-etal-2022-codec | Codec at {S}em{E}val-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.93/ | Mahran, Ahmed and Alessandro Borella, Carlo and Perifanos, Konstantinos | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 679--688 | In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Misogyny Identification) of SemEval 2022 competition. Since pretraining deep models from scratch is a resource and data hungry task, our approach is based on three main strategies. We combine different state-of-the-art architectures to capture a wide spectrum of semantic signals from the multi-modal input. We employ a multi-task learning scheme to be able to use multiple datasets from the same knowledge domain to help increase the model`s performance. We also use multiple objectives to regularize and fine tune different system components. | null | null | 10.18653/v1/2022.semeval-1.93 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,006 |
inproceedings | cordon-etal-2022-i2c | {I}2{C} at {S}em{E}val-2022 Task 5: Identification of misogyny in internet memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.94/ | Cordon, Pablo and Gonzalez Diaz, Pablo and Mata, Jacinto and Pach{\'o}n, Victoria | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 689--694 | In this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification. In our experiments we compared several architectures based on deep learning algorithms with various other approaches to binary classification using Transformers, combined with a nudity image detection algorithm to provide better results. With this approach, we achieved an F1-score of 0.665 in the evaluation process | null | null | 10.18653/v1/2022.semeval-1.94 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,007 |
inproceedings | lorentz-moreira-2022-inf | {INF}-{UFRGS} at {S}em{E}val-2022 Task 5: analyzing the performance of multimodal models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.95/ | Lorentz, Gustavo and Moreira, Viviane | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 695--699 | This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide communication. The goal of the task is to improve the quality of existing methods for misogyny identification, many of which require dedicated personnel, hence the need for automation. We experimented with five existing models, including ViLBERT and Visual BERT - both uni and multimodally pretrained - and MMBT. The datasets consist of memes with captions in English. The results show that all models achieved Macro-F1 scores above 0.64. ViLBERT was the best performer with a score of 0.698. | null | null | 10.18653/v1/2022.semeval-1.95 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,008 |
inproceedings | gu-etal-2022-mmvae | {MMVAE} at {S}em{E}val-2022 Task 5: A Multi-modal Multi-task {VAE} on Misogynous Meme Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.96/ | Gu, Yimeng and Castro, Ignacio and Tyson, Gareth | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 700--710 | Nowadays, memes have become quite common in day-to-day communications on social media platforms. They appear to be amusing, evoking and attractive to audiences. However, some memes containing malicious contents can be harmful to the targeted group and arouse public anger in the long run. In this paper, we study misogynous meme detection, a shared task in SemEval 2022 - Multimedia Automatic Misogyny Identification (MAMI). The challenge of misogynous meme detection is to co-represent multi-modal features. To tackle with this challenge, we propose a Multi-modal Multi-task Variational AutoEncoder (MMVAE) to learn an effective co-representation of visual and textual features in the latent space, and determine if the meme contains misogynous information and identify its fine-grained categories. Our model achieves 0.723 on sub-task A and 0.634 on sub-task B in terms of $F_{1}$ scores. We carry out comprehensive experiments on our model`s architecture and show that our approach significantly outperforms several strong uni-modal and multi-modal approaches. Our code is released on github. | null | null | 10.18653/v1/2022.semeval-1.96 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,009 |
inproceedings | li-etal-2022-ams | {AMS}{\_}{ADRN} at {S}em{E}val-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.97/ | Li, Da and Yi, Ming and He, Yukai | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 711--717 | Women are influential online, especially in image-based social media such as Twitter and Instagram. However, many in the network environment contain gender discrimination and aggressive information, which magnify gender stereotypes and gender inequality. Therefore, the filtering of illegal content such as gender discrimination is essential to maintain a healthy social network environment. In this paper, we describe the system developed by our team for SemEval-2022Task 5: Multimedia Automatic Misogyny Identification. More specifically, we introduce two novel system to analyze these posts: a multimodal multi-task learning architecture that combines Bertweet for text encoding with ResNet-18 for image representation, and a single-flow transformer structure which combines text embeddings from BERT-Embeddings and image embeddings from several different modules such as EfficientNet and ResNet. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the two subtasks of the current competition, ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706 macro F1-score) while exceeding the official baseline results by high margins. | null | null | 10.18653/v1/2022.semeval-1.97 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,010 |
inproceedings | kalkenings-mandl-2022-university | {U}niversity of {H}ildesheim at {S}em{E}val-2022 task 5: Combining Deep Text and Image Models for Multimedia Misogyny Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.98/ | Kalkenings, Milan and Mandl, Thomas | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 718--723 | This paper describes the participation of the University of Hildesheim at the SemEval task 5. The task deals with Multimedia Automatic Misogyny Identification (MAMI). Hateful memes need to be detected within a data collection. For this task, we implemented six models for text and image analysis and tested the effectiveness of their combinations. A fusion system implements a multi-modal transformer to integrate the embeddings of these models. The best performing models included BERT for the text of the meme, manually derived associations for words in the memes and a Faster R-CNN network for the image. We evaluated the performance of our approach also with the data of the Facebook Hateful Memes challenge in order to analyze the generalisation capabilities of the approach. | null | null | 10.18653/v1/2022.semeval-1.98 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,011 |
inproceedings | behzadi-etal-2022-mitra | Mitra Behzadi at {S}em{E}val-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on {CLIP} | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.99/ | Behzadi, Mitra and Derakhshan, Ali and Harris, Ian | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 724--727 | Everyday more users are using memes on social media platforms to convey a message with text and image combined. Although there are many fun and harmless memes being created and posted, there are also ones that are hateful and offensive to particular groups of people. In this article present a novel approach based on the CLIP network to detect misogynous memes and find out the types of misogyny in that meme. We participated in Task A and Task B of the Multimedia Automatic Misogyny Identification (MaMi) challenge and our best scores are 0.694 and 0.681 respectively. | null | null | 10.18653/v1/2022.semeval-1.99 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,012 |
inproceedings | sharma-etal-2022-iitr | {IITR} {C}ode{B}usters at {S}em{E}val-2022 Task 5: Misogyny Identification using Transformers | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.100/ | Sharma, Gagan and Sunil Gitte, Gajanan and Goyal, Shlok and Sharma, Raksha | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 728--732 | This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition. The purpose of the task is to identify given memes as misogynistic or not and further label the type of misogyny involved. In this paper, we present our approach based on language processing tools. We embed meme texts using GloVe embedding and classify misogyny using BERT model. Our model obtains an F1-score of 66.24{\%} and 63.5{\%} in misogyny classification and misogyny labels, respectively. | null | null | 10.18653/v1/2022.semeval-1.100 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,013 |
inproceedings | barnwal-etal-2022-iit | {IIT} {DHANBAD} {CODECHAMPS} at {S}em{E}val-2022 Task 5: {MAMI} - Multimedia Automatic Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.101/ | Barnwal, Shubham and Kumar, Ritesh and Pamula, Rajendra | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 733--735 | With the growth of the internet, the use of social media based on images has drastically increased like Twitter, Instagram, etc. In these social media, women have a very high contribution as of 75{\%} women use social media multiple times compared to men which is only 65{\%} of men uses social media multiple times a day. However, with this much contribution, it also increases systematic inequality and discrimination offline is replicated in online spaces in the form of MEMEs. A meme is essentially an image characterized by pictorial content with an overlaying text a posteriori introduced by humans, with the main goal of being funny and/or ironic. Although most of them are created with the intent of making funny jokes, in a short time people started to use them as a form of hate and prejudice against women, landing to sexist and aggressive messages in online environments that subsequently amplify the sexual stereotyping and gender inequality of the offline world. This leads to the need for automatic detection of Misogyny MEMEs. Specifically, I described the model submitted for the shared task on Multimedia Automatic Misogyny Identification (MAMI) and my team name is IIT DHANBAD CODECHAMPS. | null | null | 10.18653/v1/2022.semeval-1.101 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,014 |
inproceedings | gu-etal-2022-qinian | {Q}i{N}i{A}n at {S}em{E}val-2022 Task 5: Multi-Modal Misogyny Detection and Classification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.102/ | Gu, Qin and Meisinger, Nino and Dick, Anna-Katharina | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 736--741 | In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not. This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations. The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate. Our submissions perform above all organizer submitted baselines. For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665. For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637. | null | null | 10.18653/v1/2022.semeval-1.102 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,015 |
inproceedings | garcia-diaz-etal-2022-umuteam-semeval | {UMUT}eam at {S}em{E}val-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.103/ | Garc{\'i}a-D{\'i}az, Jos{\'e} and Caparros-Laiz, Camilo and Valencia-Garc{\'i}a, Rafael | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 742--747 | In this manuscript we describe the participation of the UMUTeam on the MAMI shared task proposed at SemEval 2022. This task is concerning the identification of misogynous content from a multi-modal perspective. Our participation is grounded on the combination of different feature sets within the same neural network. Specifically, we combine linguistic features with contextual transformers based on text (BERT) and images (BEiT). Besides, we also evaluate other ensemble learning strategies and the usage of non-contextual pretrained embeddings. Although our results are limited, we outperform all the baselines proposed, achieving position 36 in the binary classification task with a macro F1-score of 0.687, and position 28 in the multi-label task of misogynous categorisation, with an macro F1-score of 0.663. | null | null | 10.18653/v1/2022.semeval-1.103 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,016 |
inproceedings | han-etal-2022-ynu | {YNU}-{HPCC} at {S}em{E}val-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on {LXMERT} | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.104/ | Han, Chao and Wang, Jin and Zhang, Xuejie | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 748--755 | This paper describes our system used in the SemEval-2022 Task5 Multimedia Automatic Misogyny Identification (MAMI). This task is to use the provided text-image pairs to classify emotions. In this paper, We propose a multi-label emotion classification model based on pre-trained LXMERT. We use Faster-RCNN to extract visual representation and utilize LXMERT`s cross-attention for multi-modal alignment. Then we use the Bilinear-interaction layer to fuse these features. Our experimental results surpass the $F_1$ score of baseline. For Sub-task A, our $F_1$ score is 0.662 and Sub-task B`s $F_1$ score is 0.633. The code of this study is available on GitHub. | null | null | 10.18653/v1/2022.semeval-1.104 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,017 |
inproceedings | hakimov-etal-2022-tib | {TIB}-{VA} at {S}em{E}val-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.105/ | Hakimov, Sherzod and Cheema, Gullal Singh and Ewerth, Ralph | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 756--760 | The detection of offensive, hateful content on social media is a challenging problem that affects many online users on a daily basis. Hateful content is often used to target a group of people based on ethnicity, gender, religion and other factors. The hate or contempt toward women has been increasing on social platforms. Misogynous content detection is especially challenging when textual and visual modalities are combined to form a single context, e.g., an overlay text embedded on top of an image, also known as \textit{meme}. In this paper, we present a multimodal architecture that combines textual and visual features to detect misogynous memes. The proposed architecture is evaluated in the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification challenge under the team name TIB-VA. We obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous and further identify the following sub-classes: shaming, stereotype, objectification, and violence. | null | null | 10.18653/v1/2022.semeval-1.105 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,018 |
inproceedings | sharma-etal-2022-r2d2 | {R}2{D}2 at {S}em{E}val-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.106/ | Sharma, Mayukh and Kandasamy, Ilanthenral and W B, Vasantha | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 761--770 | This paper describes the multimodal deep learning system proposed for SemEval 2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. We participated in both Subtasks, i.e. Subtask A: Misogynous meme identification, and Subtask B: Identifying type of misogyny among potential overlapping categories (stereotype, shaming, objectification, violence). The proposed architecture uses pre-trained models as feature extractors for text and images. We use these features to learn multimodal representation using methods like concatenation and scaled dot product attention. Classification layers are used on fused features as per the subtask definition. We also performed experiments using unimodal models for setting up comparative baselines. Our best performing system achieved an F1 score of 0.757 and was ranked $3^{rd}$ in Subtask A. On Subtask B, our system performed well with an F1 score of 0.690 and was ranked $10^{th}$ on the leaderboard. We further show extensive experiments using combinations of different pre-trained models which will be helpful as baselines for future work. | null | null | 10.18653/v1/2022.semeval-1.106 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,019 |
inproceedings | huertas-garcia-etal-2022-aida | {AIDA}-{UPM} at {S}em{E}val-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.107/ | Huertas-Garc{\'i}a, {\'A}lvaro and Liz, Helena and Villar-Rodr{\'i}guez, Guillermo and Mart{\'i}n, Alejandro and Huertas-Tato, Javier and Camacho, David | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 771--779 | This paper describes the multimodal late fusion model proposed in the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) task. The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively. Additionally, our findings contribute to a better understanding of the effects of different image preprocessing methods for meme classification. We achieve 0.636 F1-macro average score for the binary subtask A, and 0.632 F1-macro average score for the multi-label subtask B. The present findings might help solve the inequality and discrimination women suffer on social media platforms. | null | null | 10.18653/v1/2022.semeval-1.107 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,020 |
inproceedings | habash-etal-2022-ymai | {YMAI} at {S}em{E}val-2022 Task 5: Detecting Misogyny in Memes using {V}isual{BERT} and {MMBT} {M}ulti{M}odal Pre-trained Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.108/ | Habash, Mohammad and Daqour, Yahya and Abdullah, Malak and Al-Ayyoub, Mahmoud | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 780--784 | This paper presents a deep learning system that contends at SemEval-2022 Task 5. The goal is to detect the existence of misogynous memes in sub-task A. At the same time, the advanced multi-label sub-task B categorizes the misogyny of misogynous memes into one of four types: stereotype, shaming, objectification, and violence. The Ensemble technique has been used for three multi-modal deep learning models: two MMBT models and VisualBERT. Our proposed system ranked 17 place out of 83 participant teams with an F1-score of 0.722 in sub-task A, which shows a significant performance improvement over the baseline model`s F1-score of 0.65. | null | null | 10.18653/v1/2022.semeval-1.108 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,021 |
inproceedings | farinango-cuervo-parde-2022-exploring | Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.109/ | Farinango Cuervo, Charic and Parde, Natalie | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 785--792 | Misogynistic memes are rampant on social media, and often convey their messages using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leveraged for the detection of misogynistic memes. Recently, researchers have turned to contrastive learning, and most notably OpenAI`s CLIP model, is an innovative solution to a variety of multimodal tasks. In this work, we experiment with contrastive learning to address the detection of misogynistic memes within the context of SemEval 2022 Task 5. Although our model does not achieve top results, these experiments provide important exploratory findings for this task. We conduct a detailed error analysis, revealing promising clues and offering a foundation for follow-up work. | null | null | 10.18653/v1/2022.semeval-1.109 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,022 |
inproceedings | srivastava-2022-poirot-semeval | Poirot at {S}em{E}val-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.110/ | Srivastava, Harshvardhan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 793--801 | In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwanted bias against them. To help alleviate this problem, we propose an early fusion model for the prediction and identification of misogynistic memes and their type in this paper for which we participated in SemEval-2022 Task 5. The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pre-trained contextual representations from different state-of-the-art transformer-based language models and pre-trained image models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competingteams and significantly outperforms the baselines. | null | null | 10.18653/v1/2022.semeval-1.110 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,023 |
inproceedings | abu-farha-etal-2022-semeval | {S}em{E}val-2022 Task 6: i{S}arcasm{E}val, Intended Sarcasm Detection in {E}nglish and {A}rabic | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.111/ | Abu Farha, Ibrahim and Oprea, Silviu Vlad and Wilson, Steven and Magdy, Walid | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 802--814 | 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. | null | null | 10.18653/v1/2022.semeval-1.111 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,024 |
inproceedings | du-etal-2022-pali | {PALI}-{NLP} at {S}em{E}val-2022 Task 6: i{S}arcasm{E}val- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.112/ | Du, Xiyang and Hu, Dou and Zhi, Jin and Jiang, Lianxin and Shi, Xiaofeng | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 815--819 | 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. | null | null | 10.18653/v1/2022.semeval-1.112 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,025 |
inproceedings | yuan-etal-2022-stce | stce at {S}em{E}val-2022 Task 6: Sarcasm Detection in {E}nglish Tweets | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.113/ | Yuan, Mengfei and Mengyuan, Zhou and Jiang, Lianxin and Mo, Yang and Shi, Xiaofeng | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 820--826 | This paper describes the systematic approach applied in {\textquotedblleft}SemEval-2022 Task 6 (iSarcasmEval) : Intended Sarcasm Detection in English and Arabic{\textquotedblright}. 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 {\textquotedblleft}SubTask-A-English{\textquotedblright} on the leaderboard). | null | null | 10.18653/v1/2022.semeval-1.113 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,026 |
inproceedings | krishnan-etal-2022-getsmartmsec | {G}et{S}mart{MSEC} at {S}em{E}val-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with {G}aussian model for Irony Type Identification | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.114/ | Krishnan, Diksha and C, Jerin Mahibha and Durairaj, Thenmozhi | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 827--833 | 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. | null | null | 10.18653/v1/2022.semeval-1.114 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,027 |
inproceedings | k-ajayan-etal-2022-amrita | {A}mrita{\_}{CEN} at {S}em{E}val-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.115/ | K Ajayan, Aparna and Mohanan, Krishna and S, Anugraha and B, Premjith and Kp, Soman | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 834--839 | 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), Na{\"ive 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. | null | null | 10.18653/v1/2022.semeval-1.115 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,028 |
inproceedings | alami-etal-2022-high | High Tech team at {S}em{E}val-2022 Task 6: Intended Sarcasm Detection for {A}rabic texts | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.116/ | Alami, Hamza and Benlahbib, Abdessamad and Alami, Ahmed | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 840--843 | 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. | null | null | 10.18653/v1/2022.semeval-1.116 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,029 |
inproceedings | el-mahdaouy-etal-2022-cs | {CS}-{UM}6{P} at {S}em{E}val-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in {E}nglish and {A}rabic | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.117/ | El Mahdaouy, Abdelkader and El Mekki, Abdellah and Essefar, Kabil and Skiredj, Abderrahman and Berrada, Ismail | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 844--850 | Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our system consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively. | null | null | 10.18653/v1/2022.semeval-1.117 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,030 |
inproceedings | v-etal-2022-techssn | {T}ech{SSN} at {S}em{E}val-2022 Task 6: Intended Sarcasm Detection using Transformer Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.118/ | V, Ramdhanush and Sivanaiah, Rajalakshmi and S, Angel and Rajendram, Sakaya Milton and T T, Mirnalinee | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 851--855 | 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. | null | null | 10.18653/v1/2022.semeval-1.118 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,031 |
inproceedings | moreno-monterde-etal-2022-i2c | {I}2{C} at {S}em{E}val-2022 Task 6: Intended Sarcasm in {E}nglish using Deep Learning Techniques | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.119/ | Moreno Monterde, Adri{\'a}n and V{\'a}zquez Ramos, Laura and Mata, Jacinto and Pach{\'o}n {\'A}lvarez, Victoria | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 856--861 | 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 | null | 10.18653/v1/2022.semeval-1.119 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,032 |
inproceedings | rahgouy-etal-2022-null | {NULL} at {S}em{E}val-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.120/ | Rahgouy, Mostafa and Babaei Giglou, Hamed and Rahgooy, Taher and Seals, Cheryl | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 862--870 | 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. | null | null | 10.18653/v1/2022.semeval-1.120 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,033 |
inproceedings | osei-brefo-liang-2022-uor | {U}o{R}-{NCL} at {S}em{E}val-2022 Task 6: Using ensemble loss with {BERT} for intended sarcasm detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.121/ | Osei-Brefo, Emmanuel and Liang, Huizhi | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 871--876 | 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. | null | null | 10.18653/v1/2022.semeval-1.121 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,034 |
inproceedings | gonzalez-diaz-etal-2022-i2c | {I}2{C} at {S}em{E}val-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.122/ | Gonzalez Diaz, Pablo and Cordon, Pablo and Mata, Jacinto and Pach{\'o}n, Victoria | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 877--880 | 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. | null | null | 10.18653/v1/2022.semeval-1.122 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,035 |
inproceedings | ashraf-etal-2022-bfcai | {BFCAI} at {S}em{E}val-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in {A}rabic Texts | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.123/ | Ashraf, Nsrin and Elkazzaz, Fathy and Taha, Mohamed and Nayel, Hamada and Elshishtawy, Tarek | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 881--884 | This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results. | null | null | 10.18653/v1/2022.semeval-1.123 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,036 |
inproceedings | mohamed-kamr-mohamed-2022-akabert | aka{BERT} at {S}em{E}val-2022 Task 6: An Ensemble Transformer-based Model for {A}rabic Sarcasm Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.124/ | Mohamed Kamr, Abdulrahman and Mohamed, Ensaf | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 885--890 | 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{\%}. | null | null | 10.18653/v1/2022.semeval-1.124 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,037 |
inproceedings | lotfy-etal-2022-alexu | {A}lex{U}-{AL} at {S}em{E}val-2022 Task 6: Detecting Sarcasm in {A}rabic Text Using Deep Learning Techniques | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.125/ | Lotfy, Aya and Torki, Marwan and El-Makky, Nagwa | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 891--895 | 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. | null | null | 10.18653/v1/2022.semeval-1.125 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,038 |
inproceedings | abdel-salam-2022-reamtchka | reamtchka at {S}em{E}val-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.126/ | Abdel-Salam, Reem | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 896--906 | 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. | null | null | 10.18653/v1/2022.semeval-1.126 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,039 |
inproceedings | singh-2022-niksss | niksss at {S}em{E}val-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ? | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.127/ | Singh, Nikhil | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 907--911 | 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 . | null | null | 10.18653/v1/2022.semeval-1.127 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,040 |
inproceedings | lad-etal-2022-dartmouth | {D}artmouth at {S}em{E}val-2022 Task 6: Detection of Sarcasm | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.128/ | Lad, Rishik and Ma, Weicheng and Vosoughi, Soroush | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 912--918 | 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. | null | null | 10.18653/v1/2022.semeval-1.128 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,041 |
inproceedings | huang-etal-2022-isd | {ISD} at {S}em{E}val-2022 Task 6: Sarcasm Detection Using Lightweight Models | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.129/ | Huang, Samantha and Chi, Ethan and Chi, Nathan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 919--922 | 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. | null | null | 10.18653/v1/2022.semeval-1.129 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,042 |
inproceedings | shaheen-nigam-2022-plumeria | Plumeria at {S}em{E}val-2022 Task 6: Sarcasm Detection for {E}nglish and {A}rabic Using Transformers and Data Augmentation | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.130/ | Shaheen, Mosab and Nigam, Shubham Kumar | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 923--937 | 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. | null | null | 10.18653/v1/2022.semeval-1.130 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,043 |
inproceedings | shekhawat-etal-2022-iiserb | {IISERB} Brains at {S}em{E}val-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in {E}nglish | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.131/ | Shekhawat, Tanuj and Kumar, Manoj and Rathore, Udaybhan and Joshi, Aditya and Patro, Jasabanta | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 938--944 | This paper describes the system architectures and the models submitted by our team {\textquotedblleft}IISERB Brains{\textquotedblright} 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. | null | null | 10.18653/v1/2022.semeval-1.131 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,044 |
inproceedings | hantsch-chkroun-2022-connotation | connotation{\_}clashers at {S}em{E}val-2022 Task 6: The effect of sentiment analysis on sarcasm detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.132/ | Hantsch, Patrick and Chkroun, Nadav | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 945--950 | 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. | null | null | 10.18653/v1/2022.semeval-1.132 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,045 |
inproceedings | angel-etal-2022-tug | {TUG}-{CIC} at {S}em{E}val-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.133/ | Angel, Jason and Aroyehun, Segun and Gelbukh, Alexander | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 951--955 | 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. | null | null | 10.18653/v1/2022.semeval-1.133 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,046 |
inproceedings | zheng-etal-2022-ynu | {YNU}-{HPCC} at {S}em{E}val-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in {E}nglish and {A}rabic | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.134/ | Zheng, Guangmin and Wang, Jin and Zhang, Xuejie | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 956--961 | In this paper, we (a YNU-HPCC team) describe the system we built in the SemEval-2022 competition. As participants in Task 6 (titled {\textquotedblleft}iSarcasmEval: Intended Sarcasm Detection In English and Arabic{\textquotedblright}), 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). | null | null | 10.18653/v1/2022.semeval-1.134 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,047 |
inproceedings | abaskohi-etal-2022-utnlp | {UTNLP} at {S}em{E}val-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.135/ | Abaskohi, Amirhossein and Rasouli, Arash and Zeraati, Tanin and Bahrak, Behnam | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 962--969 | Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The methodology and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper. We put different models, and data augmentation approaches to the test and report on which one works best. The tests begin with traditional machine learning models and progress to transformer-based and attention-based models. We employed data augmentation based on data mutation and data generation. Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-score of 0.38 in the competition`s evaluation phase. After the competition, we fixed our model`s flaws and achieved anF1-score of 0.414. | null | null | 10.18653/v1/2022.semeval-1.135 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,048 |
inproceedings | manoleasa-etal-2022-fii | {FII} {UAIC} at {S}em{E}val-2022 Task 6: i{S}arcasm{E}val - Intended Sarcasm Detection in {E}nglish and {A}rabic | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.136/ | Manoleasa, Tudor and Gifu, Daniela and Sandu, Iustin | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 970--977 | The {\textquotedblleft}iSarcasmEval - Intended Sarcasm Detection in English and Arabic{\textquotedblright} task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of textual sarcasm detection, as well as the level ofirony contained in these texts. In the contextof SemEval, we present a binary classificationmethod which classifies the text as sarcasticor non-sarcastic (task A, for English) based onfive classical machine learning approaches bytrying to train the models based on this datasetsolely (i.e., no other datasets have been used).This process indicates low performance compared to previously studied datasets, which in2dicates that the previous ones might be biased. | null | null | 10.18653/v1/2022.semeval-1.136 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,049 |
inproceedings | najafi-tavan-2022-marsan | {M}ar{S}an at {S}em{E}val-2022 Task 6: i{S}arcasm Detection via T5 and Sequence Learners | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.137/ | Najafi, Maryam and Tavan, Ehsan | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 978--986 | The paper describes SemEval-2022`s shared task {\textquotedblleft}Intended Sarcasm Detection in English and Arabic.{\textquotedblright} This task includes English and Arabic tweets with sarcasm and non-sarcasm samples and irony speech labels. The first two subtasks predict whether a text is sarcastic and the ironic category the sarcasm sample belongs to. The third one is to find the sarcastic sample from its non-sarcastic paraphrase. Deep neural networks have recently achieved highly competitive performance in many tasks. Combining deep learning with language models has also resulted in acceptable accuracy. Inspired by this, we propose a novel deep learning model on top of language models. On top of T5, this architecture uses an encoder module of the transformer, followed by LSTM and attention to utilizing past and future information, concentrating on informative tokens. Due to the success of the proposed model, we used the same architecture with a few modifications to the output layer in all three subtasks. | null | null | 10.18653/v1/2022.semeval-1.137 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,050 |
inproceedings | kaminska-etal-2022-lt3 | {LT}3 at {S}em{E}val-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.138/ | Kaminska, Olha and Cornelis, Chris and Hoste, Veronique | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 987--992 | This paper describes the approach developed by the LT3 team in the Intended Sarcasm Detection task at SemEval-2022 Task 6. We considered the binary classification subtask A for English data. The presented system is based on the fuzzy-rough nearest neighbor classification method using various text embedding techniques. Our solution reached 9th place in the official leader-board for English subtask A. | null | null | 10.18653/v1/2022.semeval-1.138 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,051 |
inproceedings | benlahbib-etal-2022-lisacteam | {LISACT}eam at {S}em{E}val-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in {E}nglish Tweets | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.139/ | Benlahbib, Abdessamad and Alami, Hamza and Alami, Ahmed | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 993--998 | In this paper, we present our system and findings for SemEval-2022 Task 6 - iSarcasmEval: Intended Sarcasm Detection in English. The main objective of this task was to identify sarcastic tweets. This task was challenging mainly due to (1) the small training dataset that contains only 3468 tweets and (2) the imbalanced class distribution (25{\%} sarcastic and 75{\%} non-sarcastic). Our submitted model (ranked eighth on Sub-Task A and fifth on Sub-Task C) consists of a Transformer-based approach (BERTweet model). | null | null | 10.18653/v1/2022.semeval-1.139 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,052 |
inproceedings | han-etal-2022-x | {X}-{P}u{D}u at {S}em{E}val-2022 Task 6: Multilingual Learning for {E}nglish and {A}rabic Sarcasm Detection | Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam | jul | 2022 | Seattle, United States | Association for Computational Linguistics | https://aclanthology.org/2022.semeval-1.140/ | Han, Yaqian and Chai, Yekun and Wang, Shuohuan and Sun, Yu and Huang, Hongyi and Chen, Guanghao and Xu, Yitong and Yang, Yang | Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) | 999--1004 | Detecting sarcasm and verbal irony from people`s subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ranked second out of 43, and ninth out of 32 in Task A: one-sentence detection in English and Arabic; fifth out of 22 in Task B: binary multi-label classification in English; first out of 16, and fifth out of 13 in Task C: sentence-pair detection in English and Arabic. | null | null | 10.18653/v1/2022.semeval-1.140 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 23,053 |
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