ymoslem/acl-abstracts huggingface dataset:
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning (2020)

Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.
Voice@SRIB at SemEval-2020 Tasks 9 and 12: Stacked Ensemblingmethod for Sentiment and Offensiveness detection in Social Media (2020)

In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we have used our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets. We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period. We stood third in Spanglish competition with our best F1-score of 0.756. Codalab username is asking28.
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers (2020)

In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1{\%} average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9{\%} average F1-Score on the test set using CodaLab username {\textquotedblleft}ahmed0sultan{\textquotedblright}.
Will\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy (2020)

Mixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of society and political trends. In this paper, we propose an ensemble of pesudo-label based Bert model and TFIDF based SGDClassifier model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich semantic information from the Bert model and word frequency information from the probabilistic ngram model to predict the sentiment of a given code-mixed tweet. Finally our team got an average F1 score of 0.731 on the final leaderboard,and our codalab username is will{\_}go.
Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations (2020)

This paper reports the zyy1510 team`s work in the International Workshop on Semantic Evaluation (SemEval-2020) shared task on Sentiment analysis for Code-Mixed (Hindi-English, English-Spanish) Social Media Text. The purpose of this task is to determine the polarity of the text, dividing it into one of the three labels positive, negative and neutral. To achieve this goal, we propose an ensemble model of word n-grams-based Multinomial Naive Bayes (MNB) and sub-word level representations in LSTM (Sub-word LSTM) to identify the sentiments of code-mixed data of Hindi-English and English-Spanish. This ensemble model combines the advantage of rich sequential patterns and the intermediate features after convolution from the LSTM model, and the polarity of keywords from the MNB model to obtain the final sentiment score. We have tested our system on Hindi-English and English-Spanish code-mixed social media data sets released for the task. Our model achieves the F1 score of 0.647 in the Hindi-English task and 0.682 in the English-Spanish task, respectively.
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media (2020)

In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task`s website.
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize? (2020)

We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences.
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020)

We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.
ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them (2020)

This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purpose of TC task was to identify an applied propaganda technique given propaganda text fragment. The goal of SI task was to find specific text fragments which contain at least one propaganda technique. Both of the developed solutions used semi-supervised learning technique of self-training. Interestingly, although CRF is barely used with transformer-based language models, the SI task was approached with RoBERTa-CRF architecture. An ensemble of RoBERTa-based models was proposed for the TC task, with one of them making use of Span CLS layers we introduce in the present paper. In addition to describing the submitted systems, an impact of architectural decisions and training schemes is investigated along with remarks regarding training models of the same or better quality with lower computational budget. Finally, the results of error analysis are presented.
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) (2020)

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models (2020)

This paper describes Galileo`s performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models. Our team participated in all three sub-tasks. In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages. We are also the only team which ranked among the top three across all languages. We also took the first place in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offence Target Identification.
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model (2020)

This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media. Given a sentence, we are asked to find out the most important words as the suggestion for automated design. We leverage the unsupervised pre-training model and finetune these models on our task. After our investigation, we found that the following models achieved an excellent performance in this task: ERNIE 2.0, XLM-ROBERTA, ROBERTA and ALBERT. We combine a pointwise regression loss and a pairwise ranking loss which is more close to the final Match m metric to finetune our models. And we also find that additional feature engineering and data augmentation can help improve the performance. Our best model achieves the highest score of 0.823 and ranks first for all kinds of metrics.
Aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning (2020)

We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training. We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.
CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection (2020)

This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86{\%}) and 8 out of 31 teams in the TC subtask (F1-score: 57.37{\%}).
Inno at SemEval-2020 Task 11: Leveraging Pure Transfomer for Multi-Class Propaganda Detection (2020)

The paper presents the solution of team {\textquotedblright}Inno{\textquotedblright} to a SEMEVAL 2020 task 11 {\textquotedblright}Detection of propaganda techniques in news articles{\textquotedblright}. The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model with an optimized learning scheme on the ability to distinguish propaganda techniques between each other. Our model showed 0:6 and 0:58 overall F1 score on validation set and test set accordingly and non-zero F1 score on each class on both sets.
NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification (2020)

This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6{\%} and a micro-averaged F-score of 58.2{\%} for those tasks accordingly.
NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo (2020)

This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plain text, containing at least one propaganda technique. The TC sub-task aims to identify the applied propaganda technique in a given text fragment. A different model was trained for each sub-task. Our best performing system for the SI task consists of pre-trained ELMo word embeddings followed by residual bidirectional LSTM network. For the TC sub-task pre-trained word embeddings from GloVe fed to a bidirectional LSTM neural network. The models achieved rank 28 among 36 teams with F1 score of 0.335 and rank 25 among 31 teams with 0.463 F1 score for SI and TC sub-tasks respectively. Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand.
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles (2020)

This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles (2020)

This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro- F 1 - score for the SI subtask is 0.406, and the micro- F 1 - score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media (2020)

This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks {--} offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.
ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection (2020)

This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media (2020)

Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives. While these applications can be valuable, they can also be exploited to promote negative opinions, insults, and hatred against a person, race, or group. These opinions can be spread to millions of people at the click of a mouse. As such, there is a need to develop mechanisms by which offensive language can be automatically detected in social media channels and managed in a timely manner. To help achieve this goal, SemEval 2020 offered a shared task (OffensEval 2020) that involved the detection of offensive text in Arabic. We propose an ensemble approach that combines different levels of word embedding models and transfers learning from other sources of emotion-related tasks. The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language Detection (2020)

In this paper we present our submission to sub-task A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2). For Danish, Turkish, Arabic and Greek, we develop an architecture based on transfer learning and relying on a two-channel BERT model, in which the English BERT and the multilingual one are combined after creating a machine-translated parallel corpus for each language in the task. For English, instead, we adopt a more standard, single-channel approach. We find that, in a multilingual scenario, with some languages having small training data, using parallel BERT models with machine translated data can give systems more stability, especially when dealing with noisy data. The fact that machine translation on social media data may not be perfect does not hurt the overall classification performance.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models (2020)

We 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 to re-train the model. We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish. Our systems obtained good results across the three languages (.9036 for EN, .7619 for DA, and .7789 for TR).
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection (2020)

Offensive 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 presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C). Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task. We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon BERT models for specific sub-tasks. Our submissions achieve F1 scores of 91.7{\%} in Sub-task A, 66.5{\%} in Sub-task B, and 63.2{\%} in Sub-task C. We perform an ablation study which reveals that domain tuning considerably improves the classification performance. Furthermore, error analysis shows common misclassification errors made by our model and outlines research directions for future.
IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language (2020)

Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C). This paper presents the results our classifiers obtained for the English language in the 3 subtasks. The classifiers used by us were BERT and BiLSTM. On the test set, our BERT classifier obtained macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers. We conjecture that the presence of few misleading instances in the dataset is affecting the performance of the classifiers. Our analysis also discusses the need of temporal context and world knowledge to determine the offensiveness of few comments.