# LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

arXiv: https://arxiv.org/abs/2003.06499 · HTML: https://mlsanity.com/paper/2003.06499/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [naab: A ready-to-use plug-and-play corpus for Farsi](https://mlsanity.com/paper/2208.13486/) (2022)
- [Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation](https://mlsanity.com/paper/2412.13375/) (2024)
- [A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora](https://mlsanity.com/paper/2211.09956/) (2022)
- [Developing an Informal-Formal Persian Corpus](https://mlsanity.com/paper/2308.05336/) (2023)
- [Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair](https://mlsanity.com/paper/2412.16877/) (2024)
- [PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification](https://mlsanity.com/paper/2602.19333/) (2026)

## Abstract (arXiv)

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a "Large Scale Colloquial Persian Dataset" (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.