PQuAD: A Persian Question Answering Dataset
This dataset addresses the lack of resources for Persian question answering, facilitating research and system development in this domain, though it is incremental as it adapts existing dataset creation methods to a new language.
The authors introduced PQuAD, a Persian question answering dataset with 80,000 questions, including 25% adversarially unanswerable ones, to serve as a benchmark for Persian reading comprehension, achieving baseline results of 74.8% EM and 87.6% F1-score with state-of-the-art models.
We present Persian Question Answering Dataset (PQuAD), a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as an MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and development of Persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.