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Found 16 papers, 0 papers with code
YMAI at SemEval-2022 Task 5: Detecting Misogyny in Memes using VisualBERT and MMBT MultiModal Pre-trained Models
This paper presents a deep learning system that contends at SemEval-2022 Task 5.
R00 at NLP4IF-2021 Fighting COVID-19 Infodemic with Transformers and More Transformers
Our proposed model has been ranked 1st with an F1-Score of 0. 780 and an Accuracy score of 0. 762.
JUST-DEEP at SemEval-2022 Task 4: Using Deep Learning Techniques to Reveal Patronizing and Condescending Language
Classification of language that favors or condones vulnerable communities (e. g., refugees, homeless, widows) has been considered a challenging task and a critical step in NLP applications.
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model
Besides, we discuss and analyze the results by comparing all the models that we trained or tested to achieve a better score in a table design.
JUST System for WMT20 Chat Translation Task
Machine Translation (MT) is a sub-field of Artificial Intelligence and Natural Language Processing that investigates and studies the ways of automatically translating a text from one language to another.
SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages
We participated in subtask A for both languages, Arabic and English.
JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models
Other studies had been designed to assess the level of word complexity using regression models or multi-labeling classification models.
SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model
At the same time, the model ranked one of the top 10 models in task 1b and task 2 with an RMSE scores of 0. 5446 and 0. 4469, respectively.
JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model
Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span.
MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)
Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website.
JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity
In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian.
TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques
We have improved the results in the post-evaluation period to achieve our best result, which would rank the 4th in the competition if we had the chance to use our latest experiment.
The 2ST-UNet for Pneumothorax Segmentation in Chest X-Rays using ResNet34 as a Backbone for U-Net
This system is built based on U-Net with Residual Networks (ResNet-34) backbone that is pre-trained on the ImageNet dataset.
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models
Defining {``}fake news{''} is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda.
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning
Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others).
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning
This paper describes TeamUNCC{'}s system to detect emotions in English and Arabic tweets.
