PQuAD: A Persian Question Answering Dataset

Abstract

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 a 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 shows 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.

A Persian Benchmark for Joint Intent Detection and Slot Filling

“…There are Persian datasets for NLP tasks like questionanswering [12], [13], [14], language modeling [19], or sentiment analysis [20]. However, there is no Persian benchmark dataset for the NLU task.…”