Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset
The Holy Qur’an is the most sacred book for more than 1.9 billion Muslims worldwide, and it provides a guide for their behaviours and daily interactions. Its miraculous eloquence and the divine essence of its verses (Khorami, 2014)(Elhindi,2017) make it far more difficult for non-scholars to answer their questions from the Qur’an. Here comes the significant role of technology in assisting all Muslims in answering their Qur’anic questions with state-of-the-art advancements in natural language processing (NLP) and information retrieval (IR). The task of constructing the finest automatic extractive Question Answering system from the Holy Qur’an with the use of the recently available Qur’anic Reading Comprehension Dataset(QRCD) was announced for LREC 2022 (Malhas et al., 2022) which opened up this new area for researchers around the world. In this paper, we propose a novel Qur’an Question Answering dataset with over 700 samples to aid future Qur’an research projects and three different approaches where we utilised self-attention based deep learning models (transformers) for building reliable intelligent question-answering systems for the Holy Qur’an that achieved a partial Reciprocal Rank (pRR) best score of 52% on the released QRCD test se
Code (0)
등록된 구현이 없습니다.
Tasks
Extractive Question-AnsweringInformation RetrievalQuestion AnsweringReading ComprehensionRetrievalSimilar Papers 제목 키워드 기반
Building Extractive Question Answering System to Support Human-AI Health Coaching Model for Sleep Domain
Non-communicable diseases (NCDs) are a leading cause of global deaths, necessitating a focus on primary prevention and lifestyle behavior change. Health coaching, coupled with Question Answering (QA) systems, has the pot…
Extractive Question-AnsweringPassage RetrievalQuestion AnsweringRetrievalGuiding Extractive Summarization with Question-Answering Rewards
Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of …
Extractive SummarizationQuestion AnsweringUNCC QA: Biomedical Question Answering system
In this paper, we detail our submission to the BioASQ competition{'}s Biomedical Semantic Question and Answering task. Our system uses extractive summarization techniques to generate answers and has scored highest ROUGE-…
Extractive SummarizationQuestion AnsweringSaved You A Click: Automatically Answering Clickbait Titles
Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation. We developed a system that will automatically f…
ArticlesXAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understanding patient medical history. However, buil…
Data AugmentationExtractive Question-AnsweringQuestion AnsweringSentence