LowResContextQA at Qur'an QA 2023 shared task: temporal and sequential representation augmented question answering span detection in Arabic

The Qur’an holds immense theological and historical significance, and developing a technology-driven solution for answering questions from this sacred text is of paramount importance. This paper presents our approach to task B of Qur’an QA 2023, part of EMNLP 2023, addressing this challenge by proposing a robust method for extracting answers from Qur’anic passages. Leveraging the Qur’anic Reading Comprehension Dataset (QRCD) v1.2, we employ innovative techniques and advanced models to improve the precision and contextuality of answers derived from Qur’anic passages. Our methodology encompasses the utilization of start and end logits, Long Short-Term Memory (LSTM) networks, and fusion mechanisms, contributing to the ongoing dialogue at the intersection of technology and spirituality.