This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context.
@article{arxiv.2307.14666,
title = {Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training},
author = {Mohammad Majd Saad Al Deen and Maren Pielka and Jörn Hees and Bouthaina Soulef Abdou and Rafet Sifa},
journal= {arXiv preprint arXiv:2307.14666},
year = {2023}
}