pwc-archive/papers-with-abstracts huggingface dataset:
Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models
Authors: Kushagri Tandon, Niladri Chatterjee
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

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.
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
Authors: Junyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, Hongfei Lin
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

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 “Patronizing and Condescending Language Detection”. 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.
HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection
Authors: Zihang Liu, Yancheng He, Feiqing Zhuang, Bing Xu
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Multi Label Text Classification, Multi-Label Text Classification, Sentence, text-classification, Text Classification

This paper describes the system for the Semeval-2022 Task4 ”Patronizing and Condescending Language Detection”.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.
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection
Authors: Wenqiang Bai, Jin Wang, Xuejie Zhang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentiment Analysis, Sentiment Classification

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.
I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques
Authors: Laura Vázquez Ramos, Adrián Moreno Monterde, Victoria Pachón, Jacinto Mata
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation, Deep Learning

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 “Task 4: Patronizing and Condescending Language Detection.” 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.
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning
Authors: Manan Suri
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

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.
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection
Authors: Alejandro Mosquera
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Data Augmentation, Meta-Learning

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.
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3
Authors: Daniel Saeedi, Sirwe Saeedi, Aliakbar Panahi, Alvis C.M. Fong
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation, Few-Shot Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence, Sentiment Analysis

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.
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language
Authors: Tudor Dumitrascu, Raluca-Andreea Gînga, Bogdan Dobre, Bogdan Radu Silviu Sielecki
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, Multi-class Classification

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.
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language
Authors: Bichu George, Adarsh S, Nishitkumar Prajapati, Premjith B, Soman Kp
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Feature Engineering, regression

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.
JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods
Authors: Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification

In this paper, we describe our submissions to SemEval-2022 subtask 4-A - “Patronizing and Condescending Language Detection: Binary Classification”. 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.
ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection
Authors: Matej Klemen, Marko Robnik-Šikonja
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Management

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.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
Authors: Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

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.
Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection
Authors: Shankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, Bharathi Raja Chakravarthi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

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.
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion
Authors: Jin Zhi, Zhou Mengyuan, Mengfei Yuan, Dou Hu, Xiyang Du, Lianxin Jiang, Yang Mo, Xiaofeng Shi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Task Learning

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).
DD-TIG at SemEval-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and Classification
Authors: Ziming Zhou, Han Zhao, Jingjing Dong, Ning Ding, Xiaolong Liu, Kangli Zhang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

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.
TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models
Authors: Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

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.
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining
Authors: Samyak Agrawal, Radhika Mamidi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Transfer Learning

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.
HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
Authors: Ayme Arango, Jesus Perez-Martin, Arniel Labrada
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

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.
SRCB at SemEval-2022 Task 5: Pretraining Based Image to Text Late Sequential Fusion System for Multimodal Misogynous Meme Identification
Authors: Jing Zhang, Yujin Wang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Image to text

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.
ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification
Authors: Ailneni Rakshitha Rao, Arjun Rao
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Meme Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

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.