Search Results for author: Mahmoud Al-Ayyoub

Found 13 papers, 4 papers with code

JUST System for WMT20 Chat Translation Task

no code implementations WMT (EMNLP) 2020 Roweida Mohammed, Mahmoud Al-Ayyoub, Malak Abdullah

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.

Machine Translation Translation

JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation

no code implementations SEMEVAL 2020 Ali Fadel, Mahmoud Al-Ayyoub, Erik Cambria

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

no code implementations6 Sep 2020 Ayat Abedalla, Malak Abdullah, Mahmoud Al-Ayyoub, Elhadj Benkhelifa

This system is built based on U-Net with Residual Networks (ResNet-34) backbone that is pre-trained on the ImageNet dataset.

Data Augmentation

Pretrained Ensemble Learning for Fine-Grained Propaganda Detection

no code implementations WS 2019 Ali Fadel, Ibraheem Tuffaha, Mahmoud Al-Ayyoub

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.

Ensemble Learning Propaganda detection

A detailed comparative study of open source deep learning frameworks

no code implementations25 Feb 2019 Ghadeer Al-Bdour, Raffi Al-Qurran, Mahmoud Al-Ayyoub, Ali Shatnawi

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

1 code implementation24 Feb 2019 Wegdan Hussien, Mahmoud Al-Ayyoub, Yahya Tashtoush, Mohammed Al-Kabi

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.

Humor Detection

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