L ow R es C ontext QA at Qur’an QA 2023 Shared Task: Temporal and Sequential Representation Augmented Question Answering Span Detection in A rabic (2023)

Abstract: 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.

## introduction the holy qur'an considered the central religious text of islam, is a source of profound wisdom, guidance, and spiritual insight for millions of people around the world @xcite . its rich and complex content spans a wide range of topics, encompassing historical narratives, moral teachings, legal principles, and metaphysical concepts @xcite . for devout muslims, seeking knowledge and understanding from the qur'an is a fundamental aspect of their faith, and it serves as a cornerstone for theological, ethical, and philosophical discourse @xcite . in the age of information technology, the quest for a deeper comprehension of the qur'an has extended beyond traditional exegesis, embracing digital tools and computational approaches @xcite @xcite @xcite . one such critical task in this domain is qur'anic question-answering (qa), which bridges the sacred text with modern technology and linguistic analysis @xcite . the goal of qur'anic qa is to enable the retrieval of specific, contextually relevant answers @xcite @xcite to a wide range of questions from the qur'an's voluminous text. this paper addresses the pressing need to develop and refine qa systems tailored for qur'anic texts. in this paper, we provide a detailed description of our system for task b of the qur'an qa 2023 shared task @xcite . the task at hand involves providing accurate, contextually appropriate answers to questions posed in modern standard arabic (msa) regarding specific qur'anic passages. these passages consist of consecutive verses from a particular surah (chapter) of the qur'an. the complexity of this task arises from the need to extract precise answers directly from the provided passage, ensuring that the responses are contextually relevant and adhere to the theological and linguistic nuances of the qur'an. our model uses start and end logits, augmented by employing two model variants. using two separate question-answering models enables us to explore different aspects of the task, capitalizing on the strengths of each model to ensure comprehensive coverage and accuracy in answer extraction. to further enhance the accuracy and relevance of our system, we pick the best start-end logits with long short-term memory (lstm) networks @xcite and multilayer perception. in this endeavor, we aim to advance both natural language processing (nlp) and the accessibility of the qur'an's profound wisdom. our work not only provides a bridge between technology and spirituality but also strives to make the wealth of knowledge contained within the qur'an more accessible to individuals seeking answers to a wide array of inquiries, whether they be of a religious, historical, or ethical nature. addi-textual, syntactic and linguistic cues tionally, it offers a valuable resource for scholars, educators, and researchers engaged in qur'anic studies, empowering them to navigate the text efficiently and extract pertinent information. ## task description we only participated in task b of qur'an qa 2023. for task b, given a specific passage from the qur'an consisting of consecutive verses within a particular surah, along with a free-text question posed in msa regarding that passage, the system's objective is to extract all answers to the question that are explicitly stated within the provided passage. the answers extracted must be in the form of text spans directly sourced from the given passage. in order to enhance the task's realism and difficulty level, some questions may not have a corresponding answer within the provided passage. in such instances, the ideal system should return no answers. conversely, when there are answers present in the passage, the system should return a ranked list of up to 10 answer spans that are relevant to the question. the evaluation measure utilized for this task is partial average precision (pap) @xcite . this metric plays a central role in assessing the performance of question-answering (qa) systems by incorporating partial matching. it acknowledges and rewards qa systems that retrieve answers that may not necessarily occupy the top rank and may only partially match one of the gold-standard answers. additionally, pap is par-ticularly well-suited for evaluating questions that may have one or more valid answers within the accompanying passage. for questions where no answer exists within the provided passage, the evaluation approach is straightforward. a "no-answer" system output is granted full credit, while any other response is assigned a score of zero. to arrive at an overall evaluation score, the pap measure is calculated and averaged across all questions, providing a comprehensive assessment of the qa system's performance. this metric is designed to capture the system's effectiveness in terms of accuracy and ranking relevance, offering a holistic view of its capabilities in the context of qur'anic text-based question-answering. ## dataset task b utilizes the qrcd (qur'anic reading comprehension dataset) v1.2. this dataset @xcite currently consists of 1,155 question-passage pairs, forming 1,399 questionpassage-answer triplets. the data split for training, development, and test sets is targeted at 70%, 10%, and 20%, respectively. a unique aspect of this dataset is the inclusion of "zero-answer questions", which make up 15% of the questions and are questions without answers in the holy qur'an. this addition aims to provide a more realistic and challenging reading comprehension task. ## system description in this section, we describe two different questionanswering models we use along with our methodology. figure 2 represents our approach for the qur'an question-answering task.