ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
Abdelrahman Abdallah Mahmoud Kasem Mahmoud Abdalla Mohamed Mahmoud Mohamed Elkasaby Yasser Elbendary Adam Jatowt
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.