الفهرس 
Background and Related workOnly 14 pages are availabe for public view
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Abstract
Transfer learning is becoming a widely used technique in the field of deep learning.
In this thesis, it is used for detecting offensive text which is becoming a prevailing
phenomenon on online social media. While the technique showed promising results
in the task of offensive text classification, the results also showed how these models
aren’t robust to simple text substitution adversarial attacks. Moreover, hate
speech is a specific type of offensive text that needs to be properly represented
in the used data sets such that the model can correctly classify them as offensive
text.
The thesis is divided into five chapters as listed below:
Chapter 1 is an introductory chapter demonstrating the motivation for using
transfer learning for Arabic offensive text classification.
Chapter 2 gives an overview of the different transfer learning techniques that
have emerged for building deep learning models especially in the field of natural
language processing.
Chapter 3 first describes the offensive and hate speech data sets that are used in
the various experiments done throughout the thesis. Then, the different transfer
learning paradigms and the adversarial attacking algorithms are described.
Chapter 4 presents the results for the transfer learning experiments on
OffensEval2020 data set. Additionally, it reports the results of attacking the best
performing model using the new adversarial attacking method. At the end of the
chapter, the zero-shot learning performance of the best performing model is
reported using three different Arabic hate speech data sets.
Chapter 5 summarises the conclusions of the thesis and provides
recommendations regarding future work that can be done to improve the
fine-tuned models and to combat the new adversarial attacking methods.