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

2023-12-07 · ArabicNLP at EMNLP 2023 · Ghazaleh Mahmoudi, Yeganeh Morshedzadeh, Sauleh Eetemadi

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%.

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Code (1)

ghazaleh-mahmoodi/Quran-QA_2023_Shared-Task

Tasks

Multi-Task LearningPassage RetrievalQuestion AnsweringReading ComprehensionRetrievalSentenceTransfer Learning

Methods 이 논문이 사용한 방법론

Triplet Loss The goal of Triplet loss, in the context of Siamese Networks, is to maximize the joint probability among all score-pairs i.e. the product of all probabilities. By using its…
Contrastive Learning 설명 없음
SimCSE SimCSE is a contrastive learning framework for generating sentence embeddings. It utilizes an unsupervised approach, which takes an input sentence and predicts itself in…

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