Work

LKAU23 at Qur’an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur’an

Year: 2023

Type: conference-paper

Abstract: The Qur'an QA 2023 shared task has two sub tasks: Passage Retrieval (PR) task and Machine Reading Comprehension (MRC) task.Our participation in the PR task was to further train several Arabic pre-trained models using a Sentence-Transformers architecture and to ensemble the best performing models.The results of the test set did not reflect the results of the development set.CL-AraBERT achieved the best results, with a 0.124 MAP.We also participate in the MRC task by further fine-tuning the base and large variants of AraBERT using Classical Arabic and Modern Standard Arabic datasets.Base AraBERT achieved the best result with the development set with a partial average precision (pAP) of 0.49, while it achieved 0.5 with the test set.In addition, we applied the ensemble approach of best performing models and post-processing steps to the final results.Our experiments with the development set showed that our proposed model achieved a 0.537 pAP.On the test set, our system obtained a pAP score of 0.49.

Source: Arabic Natural Language Processing Conference (ArabicNLP)

DOI: 10.18653/v1/2023.arabicnlp-1.80

Authors: Sarah Alnefaie, Abdullah Alsaleh, Eric Atwell, Mohammad Alsalka, Abdulrahman Altahhan

Institutions: University of Leeds, King Abdulaziz University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: King Abdulaziz University

OpenAlex ID: W4389518364