Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
- 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.
Document Type: | Preprint |
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Language: | English |
Author: | Mohammad Majd Saad Al Deen, Maren Pielka, Jörn Hees, Bouthaina Soulef Abdou, Rafet Sifa |
DOI: | https://doi.org/10.48550/arXiv.2307.14666 |
ArXiv Id: | http://arxiv.org/abs/2307.14666 |
Publisher: | arXiv |
Date of first publication: | 2023/07/27 |
Departments, institutes and facilities: | Fachbereich Informatik |
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) | |
Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren |
Entry in this database: | 2023/08/07 |