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

arXiv: https://arxiv.org/abs/2206.01550 · HTML: https://mlsanity.com/paper/2206.01550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://mlsanity.com/paper/2102.10684/) (2021)

## Built on by (most cited first, 2 of 2)

- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QA](https://mlsanity.com/paper/2401.13060/) (2024)

## Abstract (arXiv)

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.