ymoslem/acl-abstracts huggingface dataset:
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model (2020)

We describe our submitted system to the SemEval 2020. We tackled Task 12 entitled {\textquotedblleft}Multilingual Offensive Language Identification in Social Media{\textquotedblright}, 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 final submission was chosen based on the best performances which was achieved by the BERT+BiLSTM model.
IITP-AINLPML at SemEval-2020 Task 12: Offensive Tweet Identification and Target Categorization in a Multitask Environment (2020)

In this paper, we describe the participation of IITP-AINLPML team in the SemEval-2020 SharedTask 12 on Offensive Language Identification and Target Categorization in English Twitter data. Our proposed model learns to extract textual features using a BiGRU-based deep neural network supported by a Hierarchical Attention architecture to focus on the most relevant areas in the text. We leverage the effectiveness of multitask learning while building our models for sub-task A and B. We do necessary undersampling of the over-represented classes in the sub-tasks A and C.During training, we consider a threshold of 0.5 as the separation margin between the instances belonging to classes OFF and NOT in sub-task A and UNT and TIN in sub-task B. For sub-task C, the class corresponding to the maximum score among the given confidence scores of the classes(IND, GRP and OTH) is considered as the final label for an instance. Our proposed model obtains the macro F1-scores of 90.95{\%}, 55.69{\%} and 63.88{\%} in sub-task A, B and C, respectively.
INGEOTEC at SemEval-2020 Task 12: Multilingual Classification of Offensive Text (2020)

This paper describes our participation in OffensEval challenges for English, Arabic, Danish, Turkish, and Greek languages. We used several approaches, such as {\ensuremath{\mu}}TC, TextCategorization, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction from different knowledge sources to solve text classification problems.
IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation (2020)

In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.
IRLab\_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification (2020)

The paper describes systems that our team IRLab{\_}DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B.
IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM (2020)

This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro $F_1$ score of 0.779 and 0.718 for Arabic and Greek, respectively.
JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams (2020)

In this paper, we describe our submissions to SemEval-2020 contest. We tackled subtask 12 - {\textquotedblleft}Multilingual Offensive Language Identification in Social Media{\textquotedblright}. We developed different models for four languages: Arabic, Danish, Greek, and Turkish. We applied three supervised machine learning methods using various combinations of character and word n-gram features. In addition, we applied various combinations of basic preprocessing methods. Our best submission was a model we built for offensive language identification in Danish using Random Forest. This model was ranked at the 6 position out of 39 submissions. Our result is lower by only 0.0025 than the result of the team that won the 4 place using entirely non-neural methods. Our experiments indicate that char ngram features are more helpful than word ngram features. This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.
KAFK at SemEval-2020 Task 12: Checkpoint Ensemble of Transformers for Hate Speech Classification (2020)

This paper presents the approach of Team KAFK for the English edition of SemEval-2020 Task 12. We use checkpoint ensembling to create ensembles of BERT-based transformers and show that it can improve the performance of classification systems. We explore attention mask dropout to mitigate for the poor constructs of social media texts. Our classifiers scored macro-f1 of 0.909, 0.551 and 0.616 for subtasks A, B and C respectively. The code is publicly released online.
KDELAB at SemEval-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of BERT Features (2020)

In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval- 2020 Task 12. Our system precise classification by using features extracted from two different layers of a pre-trained model, the BERT-large, and ensemble them.
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT (2020)

This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language. We participated in all the provided languages for all subtasks except sub-task-A for the English language. Two main approaches have been developed the first is performed to tackle both languages Arabic and English, a weighted ensemble consists of Bi-GRU and CNN followed by Gaussian noise and global pooling layer multiplied by weights to improve the overall performance. The second is performed for other languages, a transfer learning from BERT beside the recurrent neural networks such as Bi-LSTM and Bi-GRU followed by a global average pooling layer. Word embedding and contextual embedding have been used as features, moreover, data augmentation has been used only for the Arabic language.
KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection (2020)

This paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media. We compare mono- and multilingual models based on fine-tuning pre-trained transformer models for offensive language identification in Arabic, Greek, English and Turkish. For Danish, we explore the possibility of fine-tuning a model pre-trained on a similar language, Swedish, and additionally also cross-lingual training together with English.
KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media (2020)

In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks. Our system, ranked 4th with macro averaged F1-Score of 0.897 in Arabic, 4th with score of 0.843 in Greek, and 3rd with score of 0.814 in Turkish. Additionally, we present ArabicBERT, a set of pre-trained transformer language models for Arabic that we share with the community.
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection (2020)

Nowadays, 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, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51{\%} F1 score in English Sub-task A, which is comparable to the first place (92.23{\%}F1). An empirical analysis is provided to explain the effectiveness of our approaches.
Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language (2020)

This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020. This task aims to identify and classify offensive languages in different languages on social media. We only participate in the English part of subtask A, which aims to identify offensive languages in English. To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance. Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.
LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification (2020)

This paper presents our system entitled {\textquoteleft}LIIR' for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made available by Google AI for English and non-English languages respectively. For the English language, we use a combination of two fine-tuned BERT models. For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to obtain sentence representations.
LISAC FSDM-USMBA Team at SemEval-2020 Task 12: Overcoming AraBERT`s pretrain-finetune discrepancy for Arabic offensive language identification (2020)

AraBERT is an Arabic version of the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) model. The latter has achieved good performance in a variety of Natural Language Processing (NLP) tasks. In this paper, we propose an effective AraBERT embeddings-based method for dealing with offensive Arabic language in Twitter. First, we pre-process tweets by handling emojis and including their Arabic meanings. To overcome the pretrain-finetune discrepancy, we substitute each detected emojis by the special token [MASK] into both fine tuning and inference phases. Then, we represent tweets tokens by applying AraBERT model. Finally, we feed the tweet representation into a sigmoid function to decide whether a tweet is offensive or not. The proposed method achieved the best results on OffensEval 2020: Arabic task and reached a macro F1 score equal to 90.17{\%}.
NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets (2020)

In this paper, we present the system submitted to {\textquotedblleft}SemEval-2020 Task 12{\textquotedblright}. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20{\%}, 81.82{\%} f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17{\%} and 44.51{\%} f1-score on test set respectively.
NLP\_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish (2020)

This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to identify offensive speech in social media, particularly in tweets. The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN). A multidimensional numerical representation (embedding) for each of the words in the tweets that were used by the model were determined using fastText. This allowed for using a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning. This model was used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88{\%}, 76.76{\%} and 76.70{\%}, respectively.
nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection (2020)

In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets. This year the data set for Task A is significantly larger than in the previous year. Therefore, we have adapted the BlazingText algorithm to extract embedding representation and classify texts after filtering and sanitizing the dataset according to the conventional text patterns on social media. We have gained both advantages of a speedy training process and obtained a good F1 score of 90.88{\%} on the test set. For sub-task B, we opted to fine-tune a Bidirectional Encoder Representation from a Transformer (BERT) to accommodate the limited data for categorizing offensive tweets. We have achieved an F1 score of only 56.86{\%}, but after experimenting with various label assignment thresholds in the pre-processing steps, the F1 score improved to 64{\%}.
NTU\_NLP at SemEval-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach (2020)

This paper presents our hierarchical multi-task learning (HMTL) and multi-task learning (MTL) approaches for improving the text encoder in Sub-tasks A, B, and C of Multilingual Offensive Language Identification in Social Media (SemEval-2020 Task 12). We show that using the MTL approach can greatly improve the performance of complex problems, i.e. Sub-tasks B and C. Coupled with a hierarchical approach, the performances are further improved. Overall, our best model, HMTL outperforms the baseline model by 3{\%} and 2{\%} of Macro F-score in Sub-tasks B and C of OffensEval 2020, respectively.
PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets' Pretrained Model and Noisy Student Training Method (2020)

The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media. We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification. Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method. Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team. We fine-tune the model with tweet sentences provided in the challenge.
Pin\_cod\_ at SemEval-2020 Task 12: Injecting Lexicons into Bidirectional Long Short-Term Memory Networks to Detect Turkish Offensive Tweets (2020)

This paper describes a system (pin{\_}cod{\_}) built for SemEval 2020 Task 12: OffensEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). I present the system based on the architecture of bidirectional long short-term memory networks (BiLSTM) concatenated with lexicon-based features and a social-network specific feature and then followed by two fully connected dense layers for detecting Turkish offensive tweets. The pin cod `s system achieved a macro F1-score of 0.7496 for Sub-task A - Offensive language identification in Turkish.
problemConquero at SemEval-2020 Task 12: Transformer and Soft Label-based Approaches (2020)

In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 {\textquotedblleft}Multilingual Offensive Language Identification in Social Media{\textquotedblright}. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.
SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection (2020)

This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83{\%}
Smatgrisene at SemEval-2020 Task 12: Offense Detection by AI - with a Pinch of Real I (2020)

This paper discusses how ML based classifiers can be enhanced disproportionately by adding small amounts of qualitative linguistic knowledge. As an example we present the Danish classifier Smatgrisene, our contribution to the recent OffensEval Challenge 2020. The classifier was trained on 3000 social media posts annotated for offensiveness, supplemented by rules extracted from the reference work on Danish offensive language (Rathje 2014b). Smatgrisene did surprisingly well in the competition in spite of its extremely simple design, showing an interesting trade-off between technological muscle and linguistic intelligence. Finally, we comment on the perspectives in combining qualitative and quantitative methods for NLP.
Sonal.kumari at SemEval-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models (2020)

In this paper, we present our approaches and results for SemEval-2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval 2020). The OffensEval 2020 had three subtasks: A) Identifying the tweets to be offensive (OFF) or non-offensive (NOT) for Arabic, Danish, English, Greek, and Turkish languages, B) Detecting if the offensive tweet is targeted (TIN) or untargeted (UNT) for the English language, and C) Categorizing the offensive targeted tweets into three classes, namely: individual (IND), Group (GRP), or Other (OTH) for the English language. We participate in all the subtasks A, B, and C. In our solution, first we use the pre-trained BERT model for all subtasks, A, B, and C and then we apply the BiLSTM model with attention mechanism (Attn-BiLSTM) for the same. Our result demonstrates that the pre-trained model is not giving good results for all types of languages and is compute and memory intensive whereas the Attn-BiLSTM model is fast and gives good accuracy with fewer resources. The Attn-BiLSTM model is giving better accuracy for Arabic and Greek where the pre-trained model is not able to capture the complete context of these languages due to lower vocab-size.
Ssn\_nlp at SemEval 2020 Task 12: Offense Target Identification in Social Media Using Traditional and Deep Machine Learning Approaches (2020)

Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to immense growth and usage of social media, it has an extensive reach and impact on the society. OLI is helpful for hate speech detection, flame detection and cyber bullying, hence it is used to avoid abuse and hurts. In this paper, we present state of the art machine learning approaches for OLI. We follow several approaches which include classifiers like Naive Bayes, Support Vector Machine(SVM) and deep learning approaches like Recurrent Neural Network(RNN) and Masked LM (MLM). The approaches are evaluated on the OffensEval@SemEval2020 dataset and our team ssn{\_}nlp submitted runs for the third task of OffensEval shared task. The best run of ssn{\_}nlp that uses BERT (Bidirectional Encoder Representations from Transformers) for the purpose of training the OLI model obtained F1 score as 0.61. The model performs with an accuracy of 0.80 and an evaluation loss of 1.0828. The model has a precision rate of 0.72 and a recall rate of 0.58.
SSN\_NLP\_MLRG at SemEval-2020 Task 12: Offensive Language Identification in English, Danish, Greek Using BERT and Machine Learning Approach (2020)

Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media. As an emerging growth of social media communication, offensive language detection has received more attention in the last years; we focus to perform the task on English, Danish and Greek. We have investigated which can be effect more on pre-trained models BERT (Bidirectional Encoder Representation from Transformer) and Machine Learning Approaches. Our investigation shows the difference performance between the three languages and to identify the best performance is evaluated by the classification algorithms. In the shared task SemEval-2020, our team SSN{\_}NLP{\_}MLRG submitted for three languages that are Subtasks A, B, C in English, Subtask A in Danish and Subtask A in Greek. Our team SSN{\_}NLP{\_}MLRG obtained the F1 Scores as 0.90, 0.61, 0.52 for the Subtasks A, B, C in English, 0.56 for the Subtask A in Danish and 0.67 for the Subtask A in Greek respectively.
SU-NLP at SemEval-2020 Task 12: Offensive Language IdentifiCation in Turkish Tweets (2020)

This paper summarizes our group`s efforts in the offensive language identification shared task, which is organized as part of the International Workshop on Semantic Evaluation (Sem-Eval2020). Our final submission system is an ensemble of three different models, (1) CNN-LSTM, (2) BiLSTM-Attention and (3) BERT. Word embeddings, which were pre-trained on tweets, are used while training the first two models. BERTurk, which is the first BERT model for Turkish, is also explored. Our final submitted approach ranked as the second best model in the Turkish sub-task.