GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models
This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1% partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23%.
Code (1)
Tasks
Multi-Task LearningPassage RetrievalQuestion AnsweringReading ComprehensionRetrievalSentenceTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran
We present Quran MD, a comprehensive multimodal dataset of the Quran that integrates textual, linguistic, and audio dimensions at the verse and word levels. For each verse (ayah), the dataset provides its original Arabic…
Text-To-Speech SynthesisSemantic RetrievalSpeech RecognitionStyle TransferTwo-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction
Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts. In this paper, we propose a novel two-stage framework that addresse…
Question AnsweringPassage RetrievalA computational system to handle the orthographic layer of tajwid in contemporary Quranic Orthography
Contemporary Quranic Orthography (CQO) relies on a precise system of phonetic notation that can be traced back to the early stages of Islam, when the Quran was mainly oral in nature and the first written renderings of it…
Cross-Language Approach for Quranic QA
Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance…
Machine TranslationQuestion AnsweringTranslationA Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition
Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines. However, existing ASR models often exhibit high Wo…
Self-Supervised LearningSpeech Recognition