CoChat
Sign Up
Cited by 13Topic ModelingInformation Retrieval and Search BehaviorNatural Language Processing Techniques

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Qur’an QA 2023 Shared Task: Overview of Passage Retrieval and Reading Comprehension Tasks over the Holy Qur’an

Open access
Malhas, Rana, Mansour, Watheq, Elsayed, Tamer·2023·Proceedings of ArabicNLP 2023
DOI 10.18653/v1/2023.arabicnlp-1.76
Open this paper in CoChat — free accountOpen PDF

Abstract

Motivated by the need for intelligent question answering (QA) systems on the Holy Qur'an and the success of the first Qur'an Question Answering shared task (Qur'an QA 2022 at OSACT 2022), we have organized the second version at ArabicNLP 2023.The Qur'an QA 2023 is composed of two sub-tasks: the passage retrieval (PR) task and the machine reading comprehension (MRC) task.The main aim of the shared task is to encourage state-of-the-art research on Arabic PR and MRC on the Holy Qur'an.Our shared task has attracted 9 teams to submit 22 runs for the PR task, and 6 teams to submit 17 runs for the MRC task.In this paper, we present an overview of the task and provide an outline of the approaches employed by the participating teams in both sub-tasks.mQ241 Some gold Passages: "2:87-88\t241", 2:97-101\t241", 2:102-103\t241, "2:253-254\t24 ‫السؤال:‬ ‫آن‬ ‫القر‬ ‫في‬ ‫املذكورون‬ ‫املالئكة‬ ‫هم‬ ‫من‬ ‫؟‬ Question: Who are the angels mentioned in Qur'an?

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2023.arabicnlp-1.76

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 32

  1. Basem H. Ahmed, Motaz K. Saad, and Eshrag A. Re- faee. 2022. QQATeam at Quran QA 2022: Fine- Tunning Arabic QA Models for Quran QA Task. In Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5) at the 13th Language Resources and Evaluation Con- ference (LREC 2022).
  2. Hend Al-Khalifa, Tamer Elsayed, Hamdy Mubarak, Ab- dulmohsen Al-Thubaity, Walid Magdy, and Kareem Darwish. 2022. Proceedinsg of the 5th workshop on osact with shared tasks on qur'an qa and fine- grained hate speech detection. In Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection.
  3. Hessa A. Alawwad, Lujain A. Alawwad, Jamilah Al- harbi, and Abdullah I. Alharbi. 2023. AHJL at Qur'an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Transla- tion. In Proceedings of the First Arabic Natural Language Processing Conference (ArabicNLP 2023), Singapore.
  4. Sarah Alnefaie, Abdullah N. Alsaleh, Eric Atwell, Mo- hammad Ammar Alsalka, and Abdulrahman Altah- han. 2023. LKAU23 at Qur'an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur'an. In Proceedings of the First Arabic Natural Language Processing Conference (ArabicNLP 2023), Singa- pore.
  5. Wissam Antoun, Fady Baly, and Hazem Hajj. 2020. Arabert: Transformer-based model for arabic lan- guage understanding. In LREC 2020 Workshop Lan- guage Resources and Evaluation Conference 11-16 May 2020, page 9.
  6. Wissam Antoun, Fady Baly, and Hazem Hajj. 2021. AraELECTRA: Pre-training text discriminators for Arabic language understanding. In Proceedings of the Sixth Arabic Natural Language Processing Work- shop, pages 191-195, Kyiv, Ukraine (Virtual). Asso- ciation for Computational Linguistics.
  7. Muhammad Huzaifa Bashir, Aqil M Azmi, Haq Nawaz, Wajdi Zaghouani, Mona Diab, Ala Al-Fuqaha, and Junaid Qadir. 2022. Arabic natural language pro- cessing for Qur'anic research: a systematic review. Artificial Intelligence Review, pages 1-54.
  8. Danqi Chen. 2018. Neural Reading Comprehension and Beyond. Ph.D. thesis, Stanford University.
  9. Jonathan H Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. Tydi qa: A benchmark for information-seeking question answering in typo- logically diverse languages. Transactions of the As- sociation for Computational Linguistics, 8:454-470.
  10. Mohammed Alaa Elkomy and Amany Sarhan. 2023. TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QA. In Proceedings of the First Arabic Natural Language Processing Conference (Arabic- NLP 2023), Singapore.
  11. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of sentence em- beddings. arXiv preprint arXiv:2104.08821. · arXiv
  12. Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash. 2021. The interplay of variant, size, and task type in Arabic pre-trained language models. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 92- 104, Kyiv, Ukraine (Virtual). Association for Compu- tational Linguistics.
  13. Jimmy Lin and Boris Katz. 2006. Building a reusable test collection for question answering. Journal of the American Society for Information Science and Technology, 57(7):851-861.
  14. Ghazaleh Mahmoudi and Yeganeh Morshedzadeh. 2023. GYM at Qur'an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models. In Proceedings of the First Arabic Natural Language Processing Conference (ArabicNLP 2023), Singa- pore.
  15. Rana Malhas. 2023. Arabic Question Answering on the Holy Qur'an. Ph.D. thesis, Qatar University.
  16. Rana Malhas and Tamer Elsayed. 2020. AyaTEC: Build- ing a Reusable Verse-based Test Collection for Ara- bic Question Answering on the Holy Qur'an. ACM Transactions on Asian and Low-Resource Language Information Processing, 19(6).
  17. Rana Malhas and Tamer Elsayed. 2022. Arabic Ma- chine Reading Comprehension on the Holy Qur'an using CL-AraBERT. Information Processing & Man- agement, 59(6):103068.
  18. Rana Malhas, Watheq Mansour, and Tamer Elsayed. 2022. Qur'an QA 2022: Overview of the First Shared Task on Question Answering over the Holy Qur'an. In Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5) at the 13th Language Resources and Evaluation Con- ference (LREC 2022), pages 79-87.
  19. Aly Mostafa and Omar Mohamed. 2022. GOF at Qur'an QA 2022: Towards an Efficient Question Answering For The Holy Qu'ran In The Arabic Language Using Deep Learning-Based Approach. In Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5) at the 13th Lan- guage Resources and Evaluation Conference (LREC 2022).
  20. Hussein Mozannar, Elie Maamary, Karl El Hajal, and Hazem Hajj. 2019. Neural arabic question answer- ing. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, page 108-118. As- sociation for Computational Linguistics.
  21. Adrien Pavao, Isabelle Guyon, Anne-Catherine Letour- nel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Es- calante, Sergio Escalera, Tyler Thomas, and Zhen Xu. 2023. Codalab competitions: An open source platform to organize scientific challenges. Journal of Machine Learning Research, 24(198):1-6.
  22. Damith Premasiri, Tharindu Ranasinghe, Wajdi Za- ghouani, Ruslan Mitkov, Jamal Berrich, and Toumi Bouchentouf. 2022. DTW at Qur'an QA 2022: Util- ising Transfer Learning with Transformers for Ques- tion Answering in a Low-resource Domain . In Pro- ceedings of the 5th Workshop on Open-Source Ara- bic Corpora and Processing Tools (OSACT5) at the 13th Language Resources and Evaluation Conference (LREC 2022).
  23. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084. · arXiv
  24. Ali Safaya, Moutasem Abdullatif, and Deniz Yuret. 2020. Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech identification in social media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2054-2059.
  25. Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nalla- pati, Zhiheng Huang, and Bing Xiang. 2020. Beyond [cls] through ranking by generation. arXiv preprint arXiv:2010.03073. · arXiv
  26. Hariram Veeramani and Kaushik Roy. 2023. LowResContextQA at Qur'an QA 2023 Shared Task: Temporal and Sequential Representation Augmented Question Answering Span Detection. In Proceedings of the First Arabic Natural Language Processing Conference (ArabicNLP 2023), Singapore.
  27. Kexin Wang, Nils Reimers, and Iryna Gurevych. 2021. TSDAE: Using transformer-based sequential denois- ing auto-encoderfor unsupervised sentence embed- ding learning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 671-688, Punta Cana, Dominican Republic. Associa- tion for Computational Linguistics.
  28. Ahmed Wasfey, Eman Elrefai, Muhammad Marwa, and Nawaz Haq. 2022. Stars at Qur'an QA 2022: Build- ing Automatic Extractive Question Answering Sys- tems for the Holy Qur'an with Transformer Models and Releasing a New Dataset. In Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5) at the 13th Language Resources and Evaluation Conference (LREC 2022).
  29. Andrew Yates, Rodrigo Nogueira, and Jimmy Lin. 2021. Pretrained transformers for text ranking: BERT and beyond. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies: Tutorials, pages 1-4, Online. Association for Computational Linguistics.
  30. Abdulrezzak Zekiye and Fadi Amroush. 2023. Al- jawaab at Qur'an QA 2023 shared task: Exploring Embeddings and GPT Models for Passage Retrieval and Reading Comprehension. In Proceedings of the First Arabic Natural Language Processing Confer- ence (ArabicNLP 2023), Singapore. · DOI · Paper page
  31. Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. 2021. Mr. TyDi: A multi-lingual benchmark for dense retrieval. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 127- 137, Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
  32. Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021. Retrieving and reading: A comprehensive survey on open-domain question answering. arXiv preprint arXiv:2101.00774. · arXiv

Cited by

Sorted by citations

Metadata

Authors
Malhas, R., Mansour, W., & Elsayed, T.
Affiliations
  • Qatar University (QA)
  • The University of Queensland (AU)
ORCID
Venue
Proceedings of ArabicNLP 2023
Publisher
Association for Computational Linguistics
Published
2023
Pages
690-701
DOI
10.18653/v1/2023.arabicnlp-1.76
OpenAlex
W4389518441
Cited by
13
References
3
Citations by year
2026: 3 · 2025: 4 · 2024: 1 · 2023: 5
FWCI
2.31
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Arabic Natural Language Processing Conference (ArabicNLP) (publishedVersion, cc-by)
  • Qatar University QSpace (Qatar University) (submittedVersion)
Funders
Qatar University
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
passage retrieval, machine reading comprehension, Arabic question answering, shared task, Arabic language processing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review