Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training

• URL: http://arxiv.org/abs/2307.14666v1
• Date: Thu, 27 Jul 2023 07:40:11 GMT
• Title: Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
• Authors: Mohammad Majd Saad Al Deen, Maren Pielka, J\"orn Hees, Bouthaina Soulef Abdou, Rafet Sifa
• Abstract summary: This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP) To overcome this limitation, we create a dedicated data set from publicly available resources. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches.
• Score: 0.34998703934432673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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