ymoslem/acl-abstracts huggingface dataset:
PsuedoProp at SemEval-2020 Task 11: Propaganda Span Detection Using BERT-CRF and Ensemble Sentence Level Classifier (2020)

This paper explains our teams' submission to the Shared Task of Fine-Grained Propaganda Detection in which we propose a sequential BERT-CRF based Span Identification model where the fine-grained detection is carried out only on the articles that are flagged as containing propaganda by an ensemble SLC model. We propose this setup bearing in mind the practicality of this approach in identifying propaganda spans in the exponentially increasing content base where the fine-tuned analysis of the entire data repository may not be the optimal choice due to its massive computational resource requirements. We present our analysis on different voting ensembles for the SLC model. Our system ranks 14th on the test set and 22nd on the development set and with an F1 score of 0.41 and 0.39 respectively.
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection (2020)

This paper presents a solution for the Span Identification (SI) task in the {\textquotedblleft}Detection of Propaganda Techniques in News Articles{\textquotedblright} competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.
SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features (2020)

This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the {\textquotedblleft}loaded language{\textquotedblright} and {\textquotedblleft}slogan{\textquotedblright} techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, {\textquotedblleft}flag waving{\textquotedblright} and {\textquotedblleft}appeal to fear-prejudice{\textquotedblright} have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting {\textquotedblleft}loaded language{\textquotedblright} (F1 = 0.772), {\textquotedblleft}name calling and labeling{\textquotedblright} (F1 = 0.673), {\textquotedblleft}doubt{\textquotedblright} (F1 = 0.604) and {\textquotedblleft}flag waving{\textquotedblright} (F1 = 0.543).
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles (2020)

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 {\textquotedblright}Detection of Propaganda Techniques in News Articles{\textquotedblright}. We participated in Task {\textquotedblright}Technique Classification{\textquotedblright} (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection (2020)

This 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 model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.
Team DiSaster at SemEval-2020 Task 11: Combining BERT and Hand-crafted Features for Identifying Propaganda Techniques in News (2020)

The 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 identification of propaganda techniques at the finer-grained span level. This paper describes our system submission to the subtask of technique classification (TC) for the SemEval 2020 shared task on detection of propaganda techniques in news articles. We propose a method of combining neural BERT representations with hand-crafted features via stacked generalization. Our model has the added advantage that it combines the power of contextual representations from BERT with simple span-based and article-based global features. We present an ablation study which shows that even though BERT representations are very powerful also for this task, BERT still benefits from being combined with carefully designed task-specific features.
Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning (2020)

In 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. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.
TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles (2020)

In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.
UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020)

The {\textquotedblleft}Detection of Propaganda Techniques in News Articles{\textquotedblright} 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 character n-grams. Firstly, we built a binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Secondly, we build a multilabel multiclass model to identify applied propaganda.
UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning (2020)

We describe our participation at the SemEval 2020 {\textquotedblleft}Detection of Propaganda Techniques in News Articles{\textquotedblright} - 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 results. We also investigate the potential of combining language models with resampling and ensemble learning methods to deal with data imbalance and improve performance.
UNTLing at SemEval-2020 Task 11: Detection of Propaganda Techniques in English News Articles (2020)

Our 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 detailed look at the fourteen labels and how well our semantically-focused model detects each of them. We also propose strategies to fill the gaps.
UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT (2020)

Manipulative 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. This paper describes our participation in the SemEval-2020, Task 11: Detection of PropagandaTechniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060{\%} in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302{\%} for subtask TC, ranking 19th from 32 teams.
UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection (2020)

The 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 are unable to align it with the result of the metric suggested by the organizers of the task. We test how our system handles class and feature imbalance by varying the number of samples of two classes (Propaganda and None) in the training set, the size of a context window in which a token is vectorized and combination of vectorization means. The result of our system at SemEval2020 Task 11 is F-score=0.37.
WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation (2020)

In 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 the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset.
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model (2020)

We 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.
AlexU-BackTranslation-TL at SemEval-2020 Task 12: Improving Offensive Language Detection Using Data Augmentation and Transfer Learning (2020)

Social 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 to solve the three sub-tasks associated with the dataset. Sub-task A is to determine if the tweet is toxic or not. Then, for offensive tweets, sub-task B requires determining whether the toxicity is targeted. Finally, for sub-task C, we predict the target of the offense; i.e. a group, individual, or other entity. In our solution, we tackle the problem of class imbalance in the dataset by using back translation for data augmentation and utilizing the fine-tuned BERT model in an ensemble of deep learning classifiers. We used this solution to participate in the three English sub-tasks of SemEval-2020 task 12. The proposed solution achieved 0.91393, 0.6300, and 0.57607 macro F1-average in sub-tasks A, B, and C respectively. We achieved the 9th, 14th, and 22nd places for sub-tasks A, B and C respectively.
ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media (2020)

This 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 the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.
Amsqr at SemEval-2020 Task 12: Offensive Language Detection Using Neural Networks and Anti-adversarial Features (2020)

This 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), text normalisation filters were applied at both graphical and lexical level. The normalisation step effectively mitigates not only the natural presence of lexical variants but also intentional attempts to bypass moderation by introducing out of vocabulary words. Our approach provides strong F1 scores for both 2020 (0.9134) and 2019 (0.8258) challenges.
BRUMS at SemEval-2020 Task 12: Transformer Based Multilingual Offensive Language Identification in Social Media (2020)

In this paper, we describe the team \textit{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 offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.
CoLi at UdS at SemEval-2020 Task 12: Offensive Tweet Detection with Ensembling (2020)

We 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 in an ensemble with a multilingual transformer-based model (XLM-R). The transformer model is trained on all languages combined in order to create a fully multilingual model which can leverage knowledge between languages. The machine learning model hyperparameters are fine-tuned and the statistically best performing ones included in the final ensemble.
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media (2020)

The 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 offensive and non-offensive posts, the focus of sub-task B was on the type of offensive content in the post and finally, in sub-task C, proposed systems had to identify the target of the offensive posts. The corpus for each of the languages is developed using the posts and comments over Twitter, a popular social media platform. We have participated in this challenge and submitted results for different languages. The current work presents different machine learning and deep learning techniques and analyzes their performance for offensiveness prediction which involves various classifiers and feature engineering schemes. The experimental analysis on the training set shows that SVM using language specific pre-trained word embedding (Fasttext) outperforms the other methods. Our system achieves a macro-averaged F1 score of 0.45 for Arabic language, 0.43 for Greek language and 0.54 for Turkish language.
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection (2020)

This paper describes our team work and submission for the SemEval 2020 (Sub-Task A) {\textquotedblleft}Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media{\textquotedblright}. Our two baseline models were based on different levels of representation: character vs. word level. In word level based representation we implemented a convolutional neural network model and a bi-directional GRU model. In character level based representation we implemented a hyper CNN and LSTM model. All of these models have been further augmented with attention layers for a better performance on our task. We also experimented with three types of static word embeddings: word2vec, FastText, and Glove, in addition to emoji embeddings, and compared the performance of the different deep learning models on the dataset provided by this task. The bi-directional GRU model with attention has achieved the highest score (0.85{\%} F1 score) among all other models.
Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression (2020)

This 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 available by the task organizers and used no other resources. As might be expected we did not rank highly in the comparative evaluation: 79th of 85 in task A, 34th of 43 in task B, and 24th of 39 in task C. We carried out a qualitative analysis of our results and found that the class labels in the gold standard data are somewhat noisy. We hypothesize that the extremely high accuracy ({\ensuremath{>}}{\$} 90{\%}) of the top ranked systems may reflect methods that learn the training data very well but may not generalize to the task of identifying offensive language in English. This analysis includes examples of tweets that despite being mildly redacted are still offensive.
Ferryman at SemEval-2020 Task 12: BERT-Based Model with Advanced Improvement Methods for Multilingual Offensive Language Identification (2020)

Indiscriminately posting offensive remarks on social media may promote the occurrence of negative events such as violence, crime, and hatred. This paper examines different approaches and models for solving offensive tweet classification, which is a part of the OffensEval 2020 competition. The dataset is Offensive Language Identification Dataset (OLID), which draws 14,200 annotated English Tweet comments. The main challenge of data preprocessing is the unbalanced class distribution, abbreviation, and emoji. To overcome these issues, methods such as hashtag segmentation, abbreviation replacement, and emoji replacement have been adopted for data preprocessing approaches. The main task can be divided into three sub-tasks, and are solved by Term Frequency{--}Inverse Document Frequency(TF-IDF), Bidirectional Encoder Representation from Transformer (BERT), and Multi-dropout respectively. Meanwhile, we applied different learning rates for different languages and tasks based on BERT and non-BERTmodels in order to obtain better results. Our team Ferryman ranked the 18th, 8th, and 21st with F1-score of 0.91152 on the English Sub-task A, Sub-task B, and Sub-task C, respectively. Furthermore, our team also ranked in the top 20 on the Sub-task A of other languages.
Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media (2020)

SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification inSocial Media (Zampieri et al., 2020). The task was subdivided into multiple languages anddatasets were provided for each one. The task was further divided into three sub-tasks: offensivelanguage identification, automatic categorization of offense types, and offense target identification.I participated in the task-C, that is, offense target identification. For preparing the proposed system,I made use of Deep Learning networks like LSTMs and frameworks like Keras which combine thebag of words model with automatically generated sequence based features and manually extractedfeatures from the given dataset. My system on training on 25{\%} of the whole dataset achieves macro averaged f1 score of 47.763{\%}.
Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing (2020)

In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes. We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.
I2C at SemEval-2020 Task 12: Simple but Effective Approaches to Offensive Speech Detection in Twitter (2020)

This paper describes the systems developed for I2C Group to participate on Subtasks A and B in English, and Subtask A in Turkish and Arabic in OffensEval (Task 12 of SemEval 2020). In our experiments we compare three architectures we have developed, two based on Transformer and the other based on classical machine learning algorithms. In this paper, the proposed architectures are described, and the results obtained by our systems are presented.