This study shows how the use of techniques to transfer learning from languages with high resources to languages with low resources provides an important performance increase: a multilingual BERT model fine tuned on a mixed English/Italian dataset (using for the English a literature dataset and for the Italian a reviews dataset created ad-hoc from the well-known platform TripAdvisor), provides much higher performance than models specific to Italian. At the same time, models that were pre-trained for multiple languages became widespread, providing greater flexibility of use in exchange for lower performance. For these reasons, countless versions were developed to meet the specific needs of each language, especially in the case of languages with relatively few datasets available. These models are generally pre-trained on large text corpora and only later specialized according to the precise task to be faced on much smaller amounts of data. The arrival of the Google BERT language model has confirmed the superiority of models based on a particular structure of artificial neural network called Transformer, from which many variants have resulted. Over the years, the attention of the scientific world towards the techniques of sentiment analysis has increased considerably, driven by industry. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly.

Tagging numbers in different languages