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Related Experiment Video

Updated: Sep 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Deep learning-based approach for Arabic open domain question answering.

Kholoud Alsubhi1, Amani Jamal1, Areej Alhothali1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Peerj. Computer Science
|May 31, 2022
PubMed
Summary

This study introduces a deep learning model for Arabic open-domain question answering (OpenQA). The model significantly improves passage retrieval accuracy over traditional methods, enhancing Arabic question answering capabilities.

Keywords:
Arabic open domain question answeringDense information retrieval approachTransformer-based models for question answering

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Open-domain question answering (OpenQA) is a key challenge in NLP.
  • Deep learning has advanced English OpenQA, but Arabic OpenQA lags, often using traditional methods.
  • Existing Arabic OpenQA systems lack the performance of English counterparts.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for Arabic open-domain question answering.
  • To improve passage retrieval and answer extraction for Arabic questions.
  • To bridge the performance gap between English and Arabic OpenQA systems.

Main Methods:

  • Implemented a deep learning model for Arabic OpenQA.
  • Utilized a dense passage retriever for document retrieval from resources like Wikipedia.
  • Employed the AraELECTRA model for the answer reading comprehension task.

Main Results:

  • The dense passage retriever outperformed traditional TF-IDF in top-20 passage retrieval accuracy.
  • The end-to-end question answering system showed improvement on two Arabic benchmark datasets.
  • Demonstrated the effectiveness of deep learning for Arabic OpenQA.

Conclusions:

  • Deep learning methods, specifically dense passage retrieval and AraELECTRA, are effective for Arabic OpenQA.
  • The proposed system offers a significant advancement over traditional approaches for Arabic question answering.
  • Further research can build upon these deep learning techniques to enhance multilingual OpenQA.