Low-Resource Language Processing

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42 papers in the last four weeks, up 62% on the four weeks before. 0.4% of all new papers.

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Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP
May 17, 2026Zhiyin Tan, Changxu DuanLow-Resource Language Processing
L3S Research Center, Leibniz University Hannover, Appelstraße 9A, 30167 Hannover, Germany · Technische Universität Darmstadt, Residenzschloss 1, 64283 Darmstadt, Germany
Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
May 16, 2026Firoj Alam, Shammur Absar Chowdhury, Enamul Hoque PrinceCross-Modal Alignment Multimodal Large Language Models
Qatar Computing Research Institute, HBKU, Qatar · York University, Canada
From Flat Language Labels to Typological Priors: Structured Language Conditioning for Multilingual Speech-to-Speech Translation
May 15, 2026Yu Pan, Yang Hou, Xiongfei Wu +4Speech Translation Speech Language Models
School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan · Recho Inc., Tokyo, Japan · National Institute of Informatics, Tokyo, Japan +4
ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World
May 14, 2026Ziyin Zhang, Zihan Liao, Hang Yu +2Multilingual Language Models Text Embeddings
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · Ant Group, Hangzhou, China.
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax
May 14, 2026Zeli Su, Ziyin Zhang, Zhou Liu +7Reinforcement Learning Low-Resource MT
Minzu University of China · Ant Group · Shanghai Jiao Tong University +4
WARDEN: Endangered Indigenous Language Transcription and Translation with 6 Hours of Training Data
May 13, 2026Ziheng Zhang, Yunzhong Hou, Naijing Liu +1Low-Resource MT Low-Resource Language Processing
Australian National University · University of Oxford
Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP
May 13, 2026Ruan Visser, Trienko Grobler, Marcel DunaiskiLanguage Model Pretraining Low-Resource Language Processing
Department of Computer Science, Stellenbosch University, Stellenbosch, South Africa
Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
May 13, 2026Paul Jeha, Anastasiia Sedova, Louis Béthune +4Language Model Pretraining Multilingual Language Models
1Apple · 2DTU
DocAtlas: Multilingual Document Understanding Across 80+ Languages
May 12, 2026Ahmed Heakl, Youssef Mohamed, Abdullah Sohail +6Document Layout Analysis Low-Resource Language Processing
♠MBZUAI · ♡IBM Research
Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward
May 12, 2026Nigar Alishzade, Gulchin AbdullayevaSign Language Translation Sign Language Recognition
Engineering Faculty of Karabakh University, Khankendi, Azerbaijan · MSERA Institute of Mathematics, Baku, Azerbaijan
Towards Visually-Guided Movie Subtitle Translation for Indic Languages
May 12, 2026Tarun Chintada, Kshetrimayum Boynao Singh, Asif EkbalLow-Resource MT Low-Resource Language Processing
Department of Computer Science and Engineering Indian Institute of Technology Patna, India
Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
May 11, 2026Fred Philippy, Siwen Guo, Jacques Klein +1Low-Resource Language Processing Cross-Lingual Transfer
1SnT, University of Luxembourg, Luxembourg · 2Luxembourg Institute of Science and Technology, Luxembourg
cantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu
May 10, 2026Andrew Li, Sidney WongDomain Adaptation Text Classification
Lake Washington School District · University of Otago, New Zealand · Te P¯unaha Matatini, New Zealand
Crosslingual On-Policy Self-Distillation for Multilingual Reasoning
May 10, 2026Yihong Liu, Raoyuan Zhao, Michael A. Hedderich +1On-Policy Self-Distillation Cross-Lingual Reasoning in Language Models
Center for Information and Language Processing, LMU Munich · Munich Center for Machine Learning (MCML)
Align and Shine: Building High-Quality Sentence-Aligned Corpora for Multilingual Text Simplification
May 10, 2026Kenji Hilasaca, Nouran Khallaf, Serge SharoffLow-Resource Language Processing
Centre for Translation, Localisation and Interpreting Studies School of Languages, Cultures and Societies University of Leeds, UK
From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages
May 9, 2026Matthias Schöffel, Esteban Garces AriasMultilingual Language Model Evaluation Morphosyntactic Tagging
Bavarian Academy of Sciences (BAdW), Munich, Germany · Department of Statistics, LMU Munich, Germany · Munich Center for Machine Learning (MCML), LMU Munich, Germany
Character-Level Transformer for Tajik-Persian Transliteration with a Parallel Lexical Corpus
May 9, 2026Mullosharaf K. ArabovLow-Resource Language Processing
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia
Language-Conditioned Visual Grounding with CLIP Multilingual
May 9, 2026J. de Curtò, Mauro Liz, I. de ZarzàVLM Evaluation Multilingual VLM Evaluation
Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, Barcelona, Spain · Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, Barcelona, Spain · Escuela T´ecnica Superior de Ingenier´ıa (ICAI), Universidad Pontificia Comillas, Madrid, Spain +2
LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language
May 9, 2026Luca BalloreLanguage Model Pretraining Adapter Tuning
TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP
May 7, 2026Mullosharaf K. ArabovLow-Resource Language Processing Cross-Lingual Transfer
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia
Linear Semantic Segmentation for Low-Resource Spoken Dialects
May 7, 2026Kirill Chirkunov, Younes Samih, Abed Alhakim Freihat +1Arabic NLP Speech Processing
Mohamed bin Zayed University of Artificial Intelligence · IBM Research AI
In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs
May 7, 2026Ndeye-Emilie MbengueAnalogical Reasoning Low-Resource Language Processing
WIMMICS · Université Côte d’Azur, Inria, CNRS, I3S, Sophia-Antipolis, France
Which Are the Low-Resource Languages of the Semantic Web?
May 7, 2026Ndeye-Emilie Mbengue, Pierre Monnin, Miguel Couceiro +1Low-Resource Language Processing Knowledge Graphs
WIMMICS · Université Côte d’Azur, Inria, CNRS, I3S, Sophia-Antipolis, France · INESC-ID +1
Adapting Large Language Models to a Low-Resource Agglutinative Language: A Comparative Study of LoRA and QLoRA for Bashkir
May 6, 2026Mullosharaf K. Arabov, Svetlana S. KhaybullinaLLM Fine-Tuning Low-Resource Language Processing
Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kazan, Russia
TajikNLP: An Open-Source Toolkit for Comprehensive Text Processing of Tajik (Cyrillic Script)
May 6, 2026Mullosharif K. Arabov, Karomatullo Habibullozoda, Nurali ShirinovMorphosyntactic Tagging Low-Resource Language Processing
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia · Bokhtar State University named after Nosiri Khusrav Bokhtar, Tajikistan
Benchmarking POS Tagging for the Tajik Language: A Comparative Study of Neural Architectures on the TajPersParallel Corpus
May 6, 2026Mullosharaf K. ArabovMultilingual Language Model Evaluation Morphosyntactic Tagging
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia
Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties
May 6, 2026Jinju Kim, Haeji Jung, Youjeong Roh +2Low-Resource Language Processing Dependency Parsing
Department of Electrical and Computer Engineering, Sungkyunkwan University · Language Technologies Institute, Carnegie Mellon University · Department of Computer Science, University of British Columbia +1
A Hybrid Method for Low-Resource Named Entity Recognition
May 6, 2026Do Minh Duc, Quan Xuan Truong, Viet Tran Hong +5Named Entity Recognition Neuro-Symbolic AI
Vietnam National University, Hanoi · Center for Biodiversity Monitoring and Investigation · Hanoi University of Science and Technology
Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages
May 5, 2026Stephen E. Moore, Mich-Seth Owusu, Akwasi Asare +13Multilingual Language Model Evaluation Open-Weight Language Models
Department of Mathematics, University of Cape Coast, Cape Coast, Ghana · Ghana Natural Language Processing, Cape Coast, Cape Coast, Ghana · Khaya AI LTD Ghana
Natural Language Processing: A Comprehensive Practical Guide from Tokenisation to RLHF
May 5, 2026Mullosharaf K. ArabovLLM Fine-Tuning Low-Resource Language Processing
KAZAN (VOLGA REGION) FEDERAL UNIVERSITY · INSTITUTE OF COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES

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

Zhiyin Tan, Changxu Duan

L3S Research Center, Leibniz University Hannover, Appelstraße 9A, 30167 Hannover, Germany · Technische Universität Darmstadt, Residenzschloss 1, 64283 Darmstadt, Germany

Low-Resource Language Processing

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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).

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