Abstract
Generating natural language explanations is a challenging task in natural language processing (NLP). With recent advancements in deep learning and language modeling techniques increased attention toward explanation generation. However, generating explanations based on commonsense reasoning remains a distinct and challenging problem. While many researchers have explored automatic explanation generation using deep learning approaches, no work has been done on generating Arabic explanations. This paper addresses this issue by presenting the Arabic machine translation of the Explanations for CommonsenseQA (Arabic-ECQA) and Open Mind Common Sense (Arabic-OMCS) datasets and fine-tuning the pre-trained AraGPT-2 model to automatically generate Arabic explanations. The performance of the fine-tunes AraGPT-2 models is evaluated using STS, METEOR, and ROUGE scores. To the best of the authors knowledge, this work is the first of its kind to generate Arabic explanations. To accelerate research on Arabic NLP, we make the datasets code publicly available (https://github.com/MohamedELGhaly/Arabic-ECQA).
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Beheitt, M.E.G., Ben HajHmida, M. (2023). Generation of Arabic Commonsense Explanations. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_43
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