Ali Fadel


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JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation
Ali Fadel | Mahmoud Al-Ayyoub | Erik Cambria
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we describe our team’s (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020. We evaluate five pre-trained Transformer-based language models with various sizes against the three proposed subtasks. For the first two subtasks, the best accuracy levels achieved by our models are 92.90% and 92.30%, respectively, placing our team in the 12th and 9th places, respectively. 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 latter is only 0.16 away from the 1st place human evaluation score.


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Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
Ali Fadel | Ibraheem Tuffaha | Mahmoud Al-Ayyoub
Proceedings of the First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers

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Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification
Bashar Talafha | Ali Fadel | Mahmoud Al-Ayyoub | Yaser Jararweh | Mohammad AL-Smadi | Patrick Juola
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we describe our team’s effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. The task requires building a system capable of differentiating between 25 different Arabic dialects in addition to MSA. Our approach is simple. After preprocessing the data, we use Data Augmentation (DA) to enlarge the training data six times. We then build a language model and extract n-gram word-level and character-level TF-IDF features and feed them into an MNB classifier. Despite its simplicity, the resulting model performs really well producing the 4th highest F-measure and region-level accuracy and the 5th highest precision, recall, city-level accuracy and country-level accuracy among the participating teams.

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Pretrained Ensemble Learning for Fine-Grained Propaganda Detection
Ali Fadel | Ibraheem Tuffaha | Mahmoud Al-Ayyoub
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda

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. Our top performing system results come from applying ensemble average on three pretrained models to make their predictions. The first two models use the uncased and cased versions of Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) while the third model uses Universal Sentence Encoder (USE) (Cer et al. 2018). Out of 26 participating teams, our system is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3870 F1-score on the testing dataset.

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Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation
Ali Fadel | Ibraheem Tuffaha | Bara’ Al-Jawarneh | Mahmoud Al-Ayyoub
Proceedings of the 6th Workshop on Asian Translation

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.