ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929289721544704 |
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| author | Abdallah, Abdelrahman Kasem, Mahmoud Abdalla, Mahmoud Mahmoud, Mohamed Elkasaby, Mohamed Elbendary, Yasser Jatowt, Adam |
| author_facet | Abdallah, Abdelrahman Kasem, Mahmoud Abdalla, Mahmoud Mahmoud, Mohamed Elkasaby, Mohamed Elbendary, Yasser Jatowt, Adam |
| contents | In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look similar to answerable ones, along with additional labels of open-domain questions marks a crucial advancement in Arabic NLP resources. We also present AraDPR, the first dense passage retrieval model trained on the Arabic Wikipedia corpus, specifically designed to tackle the unique challenges of Arabic text retrieval. Furthermore, our study includes extensive benchmarking of large language models (LLMs) for Arabic question answering, critically evaluating their performance in the Arabic language context. In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP. The dataset and code are publicly accessible for further research https://github.com/DataScienceUIBK/ArabicaQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17848 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ArabicaQA: A Comprehensive Dataset for Arabic Question Answering Abdallah, Abdelrahman Kasem, Mahmoud Abdalla, Mahmoud Mahmoud, Mohamed Elkasaby, Mohamed Elbendary, Yasser Jatowt, Adam Computation and Language Information Retrieval In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look similar to answerable ones, along with additional labels of open-domain questions marks a crucial advancement in Arabic NLP resources. We also present AraDPR, the first dense passage retrieval model trained on the Arabic Wikipedia corpus, specifically designed to tackle the unique challenges of Arabic text retrieval. Furthermore, our study includes extensive benchmarking of large language models (LLMs) for Arabic question answering, critically evaluating their performance in the Arabic language context. In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP. The dataset and code are publicly accessible for further research https://github.com/DataScienceUIBK/ArabicaQA. |
| title | ArabicaQA: A Comprehensive Dataset for Arabic Question Answering |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2403.17848 |