TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models

• URL: http://arxiv.org/abs/2206.01550v1
• Date: Fri, 3 Jun 2022 13:00:48 GMT
• Title: TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models
• Authors: Mohammed ElKomy, Amany M. Sarhan
• Abstract summary: Arabic is the language of the Holy Qur'an; the sacred text for 1.8 billion people across the world. We propose an ensemble learning model based on Arabic variants of BERT models. Our system achieves a Partial Reciprocal Rank (pRR) score of 56.6% on the official test set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, we witnessed great progress in different tasks of natural language understanding using machine learning. Question answering is one of these tasks which is used by search engines and social media platforms for improved user experience. Arabic is the language of the Holy Qur'an; the sacred text for 1.8 billion people across the world. Arabic is a challenging language for Natural Language Processing (NLP) due to its complex structures. In this article, we describe our attempts at OSACT5 Qur'an QA 2022 Shared Task, which is a question answering challenge on the Holy Qur'an in Arabic. We propose an ensemble learning model based on Arabic variants of BERT models. In addition, we perform post-processing to enhance the model predictions. Our system achieves a Partial Reciprocal Rank (pRR) score of 56.6% on the official test set.

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