# Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World

arXiv: https://arxiv.org/abs/2210.08523 · HTML: https://mlsanity.com/paper/2210.08523/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 69 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://mlsanity.com/paper/2104.08726/) (2021)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Dataset Geography: Mapping Language Data to Language Users](https://mlsanity.com/paper/2112.03497/) (2021)
- [BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification](https://mlsanity.com/paper/2208.07864/) (2022)
- [Keyphrase Extraction from Disaster-related Tweets](https://mlsanity.com/paper/1910.07897/) (2019)
- [Improving Website Hyperlink Structure Using Server Logs](https://mlsanity.com/paper/1512.07258/) (2015)
- [Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting](https://mlsanity.com/paper/2011.00318/) (2020)

## Built on by (most cited first, 30 of 55)

- [Why do language models perform worse for morphologically complex languages?](https://mlsanity.com/paper/2411.14198/) (2024)
- [Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?](https://mlsanity.com/paper/2409.19151/) (2024)
- [One ruler to measure them all: Benchmarking multilingual long-context language models](https://mlsanity.com/paper/2503.01996/) (2025)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [What Drives Performance in Multilingual Language Models?](https://mlsanity.com/paper/2404.19159/) (2024)
- [Tiny Aya: Bridging Scale and Multilingual Depth](https://mlsanity.com/paper/2603.11510/) (2026)
- [Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora](https://mlsanity.com/paper/2402.07446/) (2024)
- [The Call for Socially Aware Language Technologies](https://mlsanity.com/paper/2405.02411/) (2024)
- [Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora](https://mlsanity.com/paper/2303.03750/) (2023)
- [ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus](https://mlsanity.com/paper/2403.18182/) (2024)
- [The Multilingual Divide and Its Impact on Global AI Safety](https://mlsanity.com/paper/2505.21344/) (2025)
- [Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil](https://mlsanity.com/paper/2507.18264/) (2025)
- [SinLlama -- A Large Language Model for Sinhala](https://mlsanity.com/paper/2508.09115/) (2025)
- [Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models](https://mlsanity.com/paper/2210.15184/) (2022)
- [The Art of Asking: Multilingual Prompt Optimization for Synthetic Data](https://mlsanity.com/paper/2510.19806/) (2025)
- [When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training](https://mlsanity.com/paper/2601.22851/) (2026)
- [Sinhala-English Parallel Word Dictionary Dataset](https://mlsanity.com/paper/2308.02234/) (2023)
- [Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages](https://mlsanity.com/paper/2403.08693/) (2024)
- [Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research](https://mlsanity.com/paper/2406.06021/) (2024)
- [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [Improving the quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics](https://mlsanity.com/paper/2502.19074/) (2025)
- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [Artificial intelligence is creating a new global linguistic hierarchy](https://mlsanity.com/paper/2602.12018/) (2026)
- [Exploring the Maze of Multilingual Modeling](https://mlsanity.com/paper/2310.05404/) (2023)
- [Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation](https://mlsanity.com/paper/2412.19522/) (2024)
- [LAG-MMLU: Benchmarking Frontier LLM Understanding in Latvian and Giriama](https://mlsanity.com/paper/2503.11911/) (2025)
- [NLP Security and Ethics, in the Wild](https://mlsanity.com/paper/2504.06669/) (2025)
- [SiDiaC: Sinhala Diachronic Corpus](https://mlsanity.com/paper/2509.17912/) (2025)
- [The Culture Funnel: You Can't Align What isn't in the Data](https://mlsanity.com/paper/2606.13808/) (2026)

## Abstract (arXiv)

Linguistic disparity in the NLP world is a problem that has been widely acknowledged recently. However, different facets of this problem, or the reasons behind this disparity are seldom discussed within the NLP community. This paper provides a comprehensive analysis of the disparity that exists within the languages of the world. We show that simply categorising languages considering data availability may not be always correct. Using an existing language categorisation based on speaker population and vitality, we analyse the distribution of language data resources, amount of NLP/CL research, inclusion in multilingual web-based platforms and the inclusion in pre-trained multilingual models. We show that many languages do not get covered in these resources or platforms, and even within the languages belonging to the same language group, there is wide disparity. We analyse the impact of family, geographical location, GDP and the speaker population of languages and provide possible reasons for this disparity, along with some suggestions to overcome the same.