Bashar Talafha


2020

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Multi-dialect Arabic BERT for Country-level Dialect Identification
Bashar Talafha | Mohammad Ali | Muhy Eddin Za’ter | Haitham Seelawi | Ibraheem Tuffaha | Mostafa Samir | Wael Farhan | Hussein Al-Natsheh
Proceedings of the Fifth Arabic Natural Language Processing Workshop

Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there.

2019

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Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification
Bashar Talafha | Wael Farhan | Ahmed Altakrouri | Hussein Al-Natsheh
Proceedings of the Fourth Arabic Natural Language Processing Workshop

Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84% on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams.

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Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning
Ahmad Ragab | Haitham Seelawi | Mostafa Samir | Abdelrahman Mattar | Hesham Al-Bataineh | Mohammad Zaghloul | Ahmad Mustafa | Bashar Talafha | Abed Alhakim Freihat | Hussein Al-Natsheh
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features. Our system achieves an accuracy of 69.3% macro F1-score with an improvement of 1.4% accuracy from the baseline model on the DEV dataset. Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0.12% macro F1-score behind the top ranked system.

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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.