GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models

Ghazaleh Mahmoudi, Yeganeh Morshedzadeh, Sauleh Eetemadi

2023-12-07ArabicNLP at EMNLP 2023Reading Comprehension Question Answering Transfer Learning Passage Retrieval Multi-Task Learning Retrieval

Paper PDF Code

Abstract

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%.

Related Papers

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction2025-07-18 From Roots to Rewards: Dynamic Tree Reasoning with RL2025-07-17 Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering2025-07-17 Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It2025-07-17 City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning2025-07-17 Disentangling coincident cell events using deep transfer learning and compressive sensing2025-07-17 SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation2025-07-17 HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals2025-07-17