no code implementations • EACL (WANLP) 2021 • Dalya Faraj, Malak Abdullah
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.
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.
no code implementations • SemEval (NAACL) 2022 • Mohammad Makahleh, Naba Bani Yaseen, Malak Abdullah
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.
no code implementations • SemEval (NAACL) 2022 • Mohammad Habash, Yahya Daqour, Malak Abdullah, Mahmoud Al-Ayyoub
This paper presents a deep learning system that contends at SemEval-2022 Task 5.
no code implementations • NAACL (NLP4IF) 2021 • Ahmed Qarqaz, Dia Abujaber, Malak Abdullah
Our proposed model has been ranked 1st with an F1-Score of 0. 780 and an Accuracy score of 0. 762.
no code implementations • SemEval (NAACL) 2022 • Malak Abdullah, Dalya Alnore, Safa Swedat, Jumana Khrais, Mahmoud Al-Ayyoub
We participated in subtask A for both languages, Arabic and English.
no code implementations • SEMEVAL 2021 • Dalya Faraj, Malak Abdullah
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.
no code implementations • SEMEVAL 2021 • Tuqa Bani Yaseen, Qusai Ismail, Sarah Al-Omari, Eslam Al-Sobh, Malak Abdullah
Other studies had been designed to assess the level of word complexity using regression models or multi-labeling classification models.
no code implementations • SEMEVAL 2020 • Fara Shatnawi, Malak Abdullah, Mahmoud Hammad
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.
no code implementations • SEMEVAL 2020 • Ola AlTiti, Malak Abdullah, Rasha Obiedat
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.
no code implementations • SEMEVAL 2020 • Nour Al-khdour, Mutaz Bni Younes, Malak Abdullah, Mohammad AL-Smadi
In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian.
no code implementations • SEMEVAL 2020 • Roweida Mohammed, Malak Abdullah
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.
no code implementations • 6 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.
no code implementations • WS 2019 • Hani Al-Omari, Malak Abdullah, Ola AlTiti, Samira Shaikh
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.
no code implementations • SEMEVAL 2019 • Hani Al-Omari, Malak Abdullah, Nabeel Bassam
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).
no code implementations • SEMEVAL 2018 • Malak Abdullah, Samira Shaikh
This paper describes TeamUNCC{'}s system to detect emotions in English and Arabic tweets.