构建首个大规模波斯语问答数据集NextQuAD，推动低资源语言问答发展。
Building a Rich Dataset to Empower the Persian Question Answering Systems
- 构建包含7515段文本、23918个问答对的波斯语开放域数据集。
- 基于ParsBERT与XLM-RoBERTa的模型在开发集上达到0.95 EM和0.97 F1。
- 新模型在多个基准上表现更优，适合低资源语言研究者使用。
问答系统能提供简明准确的答案。目前已有大量针对英语的稳健问答系统，但像波斯语等资源较少的语言仍缺乏标准数据集。本研究提出了一个全面的波斯语开放域数据集NextQuAD，包含7,515个上下文、23,918个问题与答案。采用ParsBERT和XLM-RoBERTa两种预训练模型进行问答建模，并通过均值逻辑值融合结果。在开发集上的评估显示，精确匹配（EM）为0.95，F1得分为0.97。为对比性能，将训练好的模型在PersianQA和ParSQuAD两个数据集上测试：在PersianQA和ParSQuAD-manual上分别提升0.39和0.14的EM，在ParSQuAD-automatic上略有下降（-0.007）。
原文摘要 · Abstract (English)
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.
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