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LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
AbstractWe present a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the SemEval 2019 Task 6 which comprises …...
Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning
AbstractThe rise of social media has made information exchange faster and easier among the people. However, in recent times, the use of offensive language has seen an upsurge in social …...
Amsqr at SemEval-2020 Task 12: Offensive Language Detection Using Neural Networks and Anti-adversarial Features
AbstractThis paper describes a method and system to solve the problem of detecting offensive language in social media using anti-adversarial features. Our submission to the SemEval-2020 task 12 challenge was …...
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
AbstractWe introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it …...
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
AbstractOffensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the …...
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection
AbstractNowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, …...
Offensive Language Detection Using Brown Clustering
AbstractIn this study, we investigate the use of Brown clustering for offensive language detection. Brown clustering has been shown to be of little use when the task involves distinguishing word …...
TAC at SemEval-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media
AbstractUsage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it. This paper deals …...
UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models
AbstractOffensive language detection is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This …...
WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
AbstractCommunicating through social platforms has become one of the principal means of personal communications and interactions. Unfortunately, healthy communication is often interfered by offensive language that can have damaging effects …...
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement
AbstractSince state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media. While several approaches have …...
Contextual-Lexicon Approach for Abusive Language Detection
AbstractSince a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and …...
OffendES: A New Corpus in Spanish for Offensive Language Research
AbstractOffensive language detection and analysis has become a major area of research in Natural Language Processing. The freedom of participation in social media has exposed online users to posts designed …...
COLD: A Benchmark for Chinese Offensive Language Detection
AbstractOffensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the …...
Measuring Geographic Performance Disparities of Offensive Language Classifiers
AbstractText classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless, many studies show that classifiers are biased regarding different languages and dialects. When measuring and discovering these …...
Offensive language detection in Hebrew: can other languages help?
AbstractUnfortunately, offensive language in social media is a common phenomenon nowadays. It harms many people and vulnerable groups. Therefore, automated detection of offensive language is in high demand and it …...
Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It?
AbstractGeneration-based data augmentation (DA) has been presented in several works as a way to improve offensive language detection. However, the effectiveness of generative DA has been shown only in limited …...
SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)
AbstractNLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language …...
Why Don’t You Do It Right? Analysing Annotators’ Disagreement in Subjective Tasks
AbstractAnnotators’ disagreement in linguistic data has been recently the focus of multiple initiatives aimed at raising awareness on issues related to ‘majority voting’ when aggregating diverging annotations. Disagreement can indeed …...