Building a Rich Dataset to Empower the Persian Question Answering Systems
This addresses the problem of limited resources for Persian language processing, providing a new dataset and model that improve performance on existing benchmarks, though it is incremental in nature.
The study tackled the lack of standard datasets for Persian question answering by creating NextQuAD, a comprehensive open-domain dataset with 7,515 contexts and 23,918 questions and answers, and applied a BERT-based model achieving 0.95 Exact Match and 0.97 F1-score on the development set.
Question answering systems provide short, precise, and specific answers to questions. So far, many robust question answering systems have been developed for English, while some languages with fewer resources, like Persian, have few numbers of standard dataset. In this study, a comprehensive open-domain dataset is presented for Persian. This dataset is called NextQuAD and has 7,515 contexts, including 23,918 questions and answers. Then, a BERT-based question answering model has been applied to this dataset using two pre-trained language models, including ParsBERT and XLM-RoBERTa. The results of these two models have been ensembled using mean logits. Evaluation on the development set shows 0.95 Exact Match (EM) and 0.97 Fl_score. Also, to compare the NextQuAD with other Persian datasets, our trained model on the NextQuAD, is evaluated on two other datasets named PersianQA and ParSQuAD. Comparisons show that the proposed model increased EM by 0.39 and 0.14 respectively in PersianQA and ParSQuAD-manual, while a slight EM decline of 0.007 happened in ParSQuAD-automatic.