Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP

• URL: http://arxiv.org/abs/2605.17442v1
• Date: Sun, 17 May 2026 13:28:03 GMT
• Title: Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP
• Abstract summary: We identify 609 unique datasets across 53 languages, of which 356 remain openly accessible through working public links.<n>Our findings suggest that multilingual data scarcity should be understood not only as a production problem, but also as a question of documentation, discoverability, and long-term accessibility.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual NLP often relies on dataset counts from centralized catalogues to characterize which languages are resource-rich or resource-poor. However, these catalogues record only one layer of dataset visibility: what has been registered or institutionally distributed. They do not necessarily reflect which datasets are created, cited, or reused in the research literature. To examine this gap, we combine a catalogue-based baseline with literature-backed evidence of dataset circulation. We introduce the Resource Density Index (RDI), defined as the number of catalogued datasets per one million speakers, and compute it for the 200 most widely spoken languages in Ethnologue. Among them, 118 languages (59%) have an average RDI of zero across the LRE Map and the Linguistic Data Consortium (LDC), and another 23 fall below 0.1, corresponding to at most one catalogued dataset per ten million speakers. We then apply an LLM-assisted citation-mining pipeline over the Semantic Scholar corpus to these 141 low-visibility languages. After manual validation and consolidation, we identify 609 unique datasets across 53 languages, of which 356 remain openly accessible through working public links. These results reveal a substantial visibility gap: many large-speaker languages appear data-poor in catalogue records yet show clear evidence of dataset activity in the research literature. Our findings suggest that multilingual data scarcity should be understood not only as a production problem, but also as a question of documentation, discoverability, and long-term accessibility. Code and data are publicly available at (https://github.com/zhiyintan/dataset-visibility-asymmetry).

Related papers

• Exploring NLP Benchmarks in an Extremely Low-Resource Setting [21.656551146954587]
  This paper focuses on Ladin, an endangered Romance language, specifically targeting the Val Badia variant.<n>We create synthetic datasets for sentiment analysis and multiple-choice question answering (MCQA) by translating monolingual Italian data.
  arXiv  Detail & Related papers  (2025-09-04T07:41:23Z)
• Natural language processing for African languages [7.884789325654572]
  dissertation focuses on languages spoken in Sub-Saharan Africa where all the indigenous languages can be regarded as low-resourced.<n>We show that the quality of semantic representations learned in word embeddings does not only depend on the amount of data but on the quality of pre-training data.<n>We develop large scale human-annotated labelled datasets for 21 African languages in two impactful NLP tasks.
  arXiv  Detail & Related papers  (2025-06-30T22:26:36Z)
• DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection [71.97939405401961]
  We introduce DCAD-2000 (Data Cleaning as Anomaly Detection), a large-scale multilingual corpus constructed from Common Crawl data and existing multilingual sources.<n>DCAD-2000 covers 2,282 languages, 46.72TB of text, and 8.63 billion documents, spanning 155 high- and medium-resource languages and 159 writing scripts.<n>By fine-tuning LLMs on DCAD-2000, we demonstrate notable improvements in data quality, robustness of the cleaning pipeline, and downstream performance.
  arXiv  Detail & Related papers  (2025-02-17T08:28:29Z)
• Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon [78.12363425794214]
  We focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets.
  arXiv  Detail & Related papers  (2024-02-03T10:41:05Z)
• GlotLID: Language Identification for Low-Resource Languages [51.38634652914054]
  GlotLID-M is an LID model that satisfies the desiderata of wide coverage, reliability and efficiency. It identifies 1665 languages, a large increase in coverage compared to prior work.
  arXiv  Detail & Related papers  (2023-10-24T23:45:57Z)
• CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages [86.90220551111096]
  Training datasets for large language models (LLMs) are often not fully disclosed. We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.
  arXiv  Detail & Related papers  (2023-09-17T23:49:10Z)
• SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects [9.501383449039142]
  We created SIB-200 -- a large-scale benchmark dataset for topic classification in 200 languages and dialects. For many of the languages covered in SIB-200, this is the first publicly available evaluation dataset for Natural Language Understanding. We found that languages unseen during the pre-training of multilingual language models, under-represented language families, and languages from the regions of Africa, Americas, Oceania and South East Asia often have the lowest performance on our topic classification dataset.
  arXiv  Detail & Related papers  (2023-09-14T05:56:49Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• An Open Dataset and Model for Language Identification [84.15194457400253]
  We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages. We make both the model and the dataset available to the research community.
  arXiv  Detail & Related papers  (2023-05-23T08:43:42Z)
• Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources [38.814057529254846]
  We examine the characteristics of 156 publicly available NLP datasets. We survey language-proficient NLP researchers and crowd workers per language. We identify strategies for collecting high-quality multilingual data on the Mechanical Turk platform.
  arXiv  Detail & Related papers  (2022-11-28T18:54:33Z)
• Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks [0.007696728525672149]
  In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families. Some of these first-of-their-kind baselines are comparable to state-of-the-art performance for higher-resourced languages.
  arXiv  Detail & Related papers  (2022-10-26T13:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.