# The Annotation Bottleneck in Persian Text NLP: Persian as an Annotation-Scarce Language

arXiv: https://arxiv.org/abs/2608.24698 · HTML: https://mlsanity.com/paper/2608.24698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 30 of 32)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://mlsanity.com/paper/2012.06154/) (2020)
- [FarsTail: A Persian Natural Language Inference Dataset](https://mlsanity.com/paper/2009.08820/) (2020)
- [Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?](https://mlsanity.com/paper/2404.06644/) (2024)
- [SentiPers: A Sentiment Analysis Corpus for Persian](https://mlsanity.com/paper/1801.07737/) (2018)
- [MIZAN: A Large Persian-English Parallel Corpus](https://mlsanity.com/paper/1801.02107/) (2018)
- [Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://mlsanity.com/paper/2012.11204/) (2020)
- [Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT](https://mlsanity.com/paper/2404.02403/) (2024)
- [A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database](https://mlsanity.com/paper/1912.03627/) (2019)
- [ArmanEmo: A Persian Dataset for Text-based Emotion Detection](https://mlsanity.com/paper/2207.11808/) (2022)
- [Persian Rhetorical Structure Theory](https://mlsanity.com/paper/2106.13833/) (2021)
- [PersianMind: A Cross-Lingual Persian-English Large Language Model](https://mlsanity.com/paper/2401.06466/) (2024)
- [Graphemic Normalization of the Perso-Arabic Script](https://mlsanity.com/paper/2210.12273/) (2022)
- [PEYMA: A Tagged Corpus for Persian Named Entities](https://mlsanity.com/paper/1801.09936/) (2018)
- [HmBlogs: A big general Persian corpus](https://mlsanity.com/paper/2111.02362/) (2021)
- [PerCQA: Persian Community Question Answering Dataset](https://mlsanity.com/paper/2112.13238/) (2021)
- [A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset](https://mlsanity.com/paper/2301.10180/) (2023)
- [Matina: A Large-Scale 73B Token Persian Text Corpus](https://mlsanity.com/paper/2502.09188/) (2025)
- [ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis](https://mlsanity.com/paper/2510.10774/) (2025)
- [NSURL-2019 Task 7: Named Entity Recognition (NER) in Farsi](https://mlsanity.com/paper/2003.09029/) (2020)
- [A hybrid entity-centric approach to Persian pronoun resolution](https://mlsanity.com/paper/2211.06257/) (2022)
- [FarsEval-PKBETS: A new diverse benchmark for evaluating Persian large language models](https://mlsanity.com/paper/2504.14690/) (2025)
- [Benchmarking Open-Source Large Language Models for Persian in Zero-Shot and Few-Shot Learning](https://mlsanity.com/paper/2510.12807/) (2025)

## Abstract (arXiv)

Persian (Farsi) is often described as a low-resource language in natural language processing, but that label collapses distinct shortages into a single category. This paper argues that Persian is more precisely described as annotation-scarce, provided that the term is understood as a property of its NLP resource ecology rather than an intrinsic property of the language. The review covers 34 representative Persian text resources available by July 2026 and adds three quantitative cross-checks. First, independent web measurements place Persian among roughly the twenty most visible content languages: W3Techs reports Persian on about 0.9% of websites with a known content language, while Common Crawl CC-MAIN-2026-30 identifies Persian as the primary language of 0.7039% of HTML pages. Second, a selective speech review shows a long resource trajectory from FARSDAT to recent corpora containing hundreds or thousands of hours of speech. Third, a matched Persian-English comparison normalizes task-specific annotation volumes by relative Common Crawl web presence. The resulting ratios vary sharply: Persian syntax and news NER are comparatively dense, whereas natural-language inference falls below the web-proportional baseline. The evidence therefore does not support a simple claim that Persian is globally deficient in labeled volume. Instead, annotation scarcity is expressed through uneven task and domain coverage, incompatible schemes, access and documentation friction, and limited supervision for specialist domains, preference data, and varieties beyond standard Iranian Persian.