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  • 2205.06025v1

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DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain

Research output: Working paper › Preprint

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DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain. / Premasiri, Damith; Ranasinghe, Tharindu; Zaghouani, Wajdi et al.
Arxiv, 2022.

Research output: Working paper › Preprint

Harvard

Premasiri, D, Ranasinghe, T, Zaghouani, W & Mitkov, R 2022 'DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain' Arxiv. <https://arxiv.org/abs/2205.06025>

APA

Premasiri, D., Ranasinghe, T., Zaghouani, W., & Mitkov, R. (2022). DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain. Arxiv. https://arxiv.org/abs/2205.06025

Vancouver

Premasiri D, Ranasinghe T, Zaghouani W, Mitkov R. DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain. Arxiv. 2022 May 12.

Author

Premasiri, Damith ; Ranasinghe, Tharindu ; Zaghouani, Wajdi et al. / DTW at Qur'an QA 2022 : Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain. Arxiv, 2022.

Bibtex

@techreport{4260b3ccf99140e98c1ce3e424dd7c73,
title = "DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain",
abstract = " The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understudied in several domains, including religious texts. The goal of the Qur'an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur'an. This paper describes the DTW entry to the Quran QA 2022 shared task. Our methodology uses transfer learning to take advantage of available Arabic MRC data. We further improve the results using various ensemble learning strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49 on the test set, proving its strong performance on the task. ",
keywords = "cs.CL",
author = "Damith Premasiri and Tharindu Ranasinghe and Wajdi Zaghouani and Ruslan Mitkov",
note = "Accepted to OSACT5 Co-located with LREC 2022",
year = "2022",
month = may,
day = "12",
language = "English",
publisher = "Arxiv",
type = "WorkingPaper",
institution = "Arxiv",

}

RIS

TY - UNPB

T1 - DTW at Qur'an QA 2022

T2 - Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain

AU - Premasiri, Damith

AU - Ranasinghe, Tharindu

AU - Zaghouani, Wajdi

AU - Mitkov, Ruslan

N1 - Accepted to OSACT5 Co-located with LREC 2022

PY - 2022/5/12

Y1 - 2022/5/12

N2 - The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understudied in several domains, including religious texts. The goal of the Qur'an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur'an. This paper describes the DTW entry to the Quran QA 2022 shared task. Our methodology uses transfer learning to take advantage of available Arabic MRC data. We further improve the results using various ensemble learning strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49 on the test set, proving its strong performance on the task.

AB - The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understudied in several domains, including religious texts. The goal of the Qur'an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur'an. This paper describes the DTW entry to the Quran QA 2022 shared task. Our methodology uses transfer learning to take advantage of available Arabic MRC data. We further improve the results using various ensemble learning strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49 on the test set, proving its strong performance on the task.

KW - cs.CL

M3 - Preprint

BT - DTW at Qur'an QA 2022

PB - Arxiv

ER -