# PCoQA: Persian Conversational Question Answering Dataset

arXiv: https://arxiv.org/abs/2312.04362 · HTML: https://mlsanity.com/paper/2312.04362/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [What Makes Reading Comprehension Questions Easier?](https://mlsanity.com/paper/1808.09384/) (2018)
- [DRCD: a Chinese Machine Reading Comprehension Dataset](https://mlsanity.com/paper/1806.00920/) (2018)
- [DoQA -- Accessing Domain-Specific FAQs via Conversational QA](https://mlsanity.com/paper/2005.01328/) (2020)
- [Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering](https://mlsanity.com/paper/1912.05200/) (2019)
- [Attentive History Selection for Conversational Question Answering](https://mlsanity.com/paper/1908.09456/) (2019)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Reinforced Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2210.15777/) (2022)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)

## Built on by (most cited first, 5 of 5)

- [FaBERT: Pre-training BERT on Persian Blogs](https://mlsanity.com/paper/2402.06617/) (2024)
- [PerkwE_COQA: Enhanced Persian Conversational Question Answering by combining contextual keyword extraction with Large Language Models](https://mlsanity.com/paper/2404.05406/) (2024)
- [Building a Rich Dataset to Empower the Persian Question Answering Systems](https://mlsanity.com/paper/2412.20212/) (2024)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)
- [IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources](https://mlsanity.com/paper/2606.20089/) (2026)

## Abstract (arXiv)

Humans seek information regarding a specific topic through performing a conversation containing a series of questions and answers. In the pursuit of conversational question answering research, we introduce the PCoQA, the first Persian Conversational Question Answering dataset, a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. Each dialog involves a questioner, a responder, and a document from the Wikipedia; The questioner asks several inter-connected questions from the text and the responder provides a span of the document as the answer for each question. PCoQA is designed to present novel challenges compared to previous question answering datasets including having more open-ended non-factual answers, longer answers, and fewer lexical overlaps. This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models. Our models include baseline models and pre-trained models, which are leveraged to boost the performance of the model. The dataset and benchmarks are available at our Github page.