Search Results for author:
Found 15 papers, 4 papers with code
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.
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.
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.
sarcasm detection and quantification in arabic tweets
The role of predicting sarcasm in the text is known as automatic sarcasm detection.
JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation
As for the last subtask, our models reach 16. 10 BLEU score and 1. 94 human evaluation score placing our team in the 5th and 3rd places according to these two metrics, respectively.
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.
Predicting Helpfulness of Online Reviews
Moreover, the semi-supervised has a remarkable performance compared with the other ones.
Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
In this paper, we describe our team's effort on the semantic text question similarity task of NSURL 2019.
Ranked #2 on
Question Similarity
on Q2Q Arabic Benchmark
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation
In this work, we present several deep learning models for the automatic diacritization of Arabic text.
Ranked #2 on
Arabic Text Diacritization
on Tashkeela
(using extra training data)
Pretrained Ensemble Learning for Fine-Grained Propaganda Detection
In this paper, we describe our team{'}s effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference.
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification
In this paper, we describe our team{'}s effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.
Arabic Text Diacritization Using Deep Neural Networks
After constructing the dataset, existing tools and systems are tested on it.
Ranked #6 on
Arabic Text Diacritization
on Tashkeela
A detailed comparative study of open source deep learning frameworks
To ensure that our study is as comprehensive as possible, we conduct several experiments using multiple benchmark datasets from different fields (image processing, NLP, etc.)
On the Use of Emojis to Train Emotion Classifiers
Nonetheless, we experimentally show that training classifiers on cheap, large and possibly erroneous data annotated using this approach leads to more accurate results compared with training the same classifiers on the more expensive, much smaller and error-free manually annotated training data.
