# PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection

arXiv: https://arxiv.org/abs/2201.06573 · HTML: https://mlsanity.com/paper/2201.06573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection](https://mlsanity.com/paper/2103.12450/) (2021)
- [Hamtajoo: A Persian Plagiarism Checker for Academic Manuscripts](https://mlsanity.com/paper/2112.13742/) (2021)

## Built on by (most cited first, 1 of 1)

- [FarSSiBERT: A Novel Transformer-based Model for Semantic Similarity Measurement of Persian Social Networks Informal Texts](https://mlsanity.com/paper/2407.19173/) (2024)

## Abstract (arXiv)

In this paper we introduce PerPaDa, a Persian paraphrase dataset that is collected from users' input in a plagiarism detection system. As an implicit crowdsourcing experience, we have gathered a large collection of original and paraphrased sentences from Hamtajoo; a Persian plagiarism detection system, in which users try to conceal cases of text re-use in their documents by paraphrasing and re-submitting manuscripts for analysis. The compiled dataset contains 2446 instances of paraphrasing. In order to improve the overall quality of the collected data, some heuristics have been used to exclude sentences that don't meet the proposed criteria. The introduced corpus is much larger than the available datasets for the task of paraphrase identification in Persian. Moreover, there is less bias in the data compared to the similar datasets, since the users did not try some fixed predefined rules in order to generate similar texts to their original inputs.