Articles

AbstractThis paper describes our team work and submission for the SemEval 2020 (Sub-Task A) “Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media”. Our two baseline models were based on different levels of representation: character vs. word level. In word level based representation we implemented a convolutional …...

AbstractThe SemEval-2020 Task 12 (OffensEval) challenge focuses on detection of signs of offensiveness using posts or comments over social media. This task has been organized for several languages, e.g., Arabic, Danish, English, Greek and Turkish. It has featured three related sub-tasks for English language: sub-task A was to discriminate between …...

AbstractWe present our submission and results for SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) where we participated in offensive tweet classification tasks in English, Arabic, Greek, Turkish and Danish. Our approach included classical machine learning architectures such as support vector machines and logistic regression combined …...

AbstractIn this paper, we describe the team BRUMS entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify …...

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 generated by an stacked ensemble of neural networks fine-tuned on the OLID dataset and additional external sources. For Task-A (English), …...

AbstractThis paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on …...

AbstractSocial media platforms, online news commenting spaces, and many other public forums have become widely known for issues of abusive behavior such as cyber-bullying and personal attacks. In this paper, we use the annotated tweets of the Offensive Language Identification Dataset (OLID) to train three levels of deep learning classifiers …...

AbstractWe describe our submitted system to the SemEval 2020. We tackled Task 12 entitled “Multilingual Offensive Language Identification in Social Media”, specifically subtask 4A-Arabic. We propose three Arabic offensive language identification models: Tw-StAR, BERT and BERT+BiLSTM. Two Arabic abusive/hate datasets were added to the training dataset: L-HSAB and T-HSAB. The …...

AbstractWe only participated in the first subtask, and a neural sequence model was used to perform the sequence tagging task. We investigated the effects of different markup strategies on model performance. Bert that performed very well in NLP was used as a feature extractor....

AbstractIn this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of …...

AbstractThe article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to quickly test different hypotheses about our data. We come up with what seems to us the best solution, however, we …...

AbstractThis paper describes the Duluth systems that participated in SemEval–2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval–2020). We participated in the three English language tasks. Our systems provide a simple machine learning baseline using logistic regression. We trained our models on the distantly supervised training data made …...

AbstractManipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. …...

AbstractThe “Detection of Propaganda Techniques in News Articles” task at the SemEval 2020 competition focuses on detecting and classifying propaganda, pervasive in news article. In this paper, we present a system able to evaluate on sentence level, three traditional text representation techniques for these study goals, using: tf*idf, word and …...

AbstractWe describe our participation at the SemEval 2020 “Detection of Propaganda Techniques in News Articles” - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementations, evaluation results and analysis of these …...

AbstractOur system for the PropEval task explores the ability of semantic features to detect and label propagandistic rhetorical techniques in English news articles. For Subtask 2, labeling identified propagandistic fragments with one of fourteen technique labels, our system attains a micro-averaged F1 of 0.40; in this paper, we take a …...

AbstractIn this paper, we present our approach for the ’Detection of Propaganda Techniques in News Articles’ task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. …...

AbstractThe identification of communication techniques in news articles such as propaganda is important, as such techniques can influence the opinions of large numbers of people. Most work so far focused on the identification at the news article level. Recently, a new dataset and shared task has been proposed for the …...

AbstractThis paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid …...

AbstractThis paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning …...