# Building a Rich Dataset to Empower the Persian Question Answering Systems

arXiv: https://arxiv.org/abs/2412.20212 · HTML: https://mlsanity.com/paper/2412.20212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://mlsanity.com/paper/1810.12885/) (2018)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [TWEETQA: A Social Media Focused Question Answering Dataset](https://mlsanity.com/paper/1907.06292/) (2019)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering](https://mlsanity.com/paper/1912.05200/) (2019)
- [Technical report on Conversational Question Answering](https://mlsanity.com/paper/1909.10772/) (2019)
- [ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion](https://mlsanity.com/paper/2006.12146/) (2020)
- [Embracing data abundance: BookTest Dataset for Reading Comprehension](https://mlsanity.com/paper/1610.00956/) (2016)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [PCoQA: Persian Conversational Question Answering Dataset](https://mlsanity.com/paper/2312.04362/) (2023)
- [Ensemble ALBERT on SQuAD 2.0](https://mlsanity.com/paper/2110.09665/) (2021)
- [IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources](https://mlsanity.com/paper/2304.11664/) (2023)

## Abstract (arXiv)

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.