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 pretraining 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 multitask pretraining in this context.
Citation:
M. Pielka, J. Hees, B. S. Abdou, R. Sifa, and M. M. S. Al Deen, “Improving natural language inference in Arabic using transformer models and linguistically informed Pre-Training,” IEEE Conference Publication | IEEE Xplore, Dec. 05, 2023. https://ieeexplore.ieee.org/document/10371891