Multilingual Text Classification

Latest papers

20 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

How Many Labels Does a Language Need? Annotation Budgets and Cross-Lingual Pooling for African-Language Text Classification
Sep 29, 2026Bhanu Prakash Vangala, Sowmya Guda, Navya VangalaClassification Low-Resource Language Processing
University of Missouri · Amar Bio Tech Pvt Ltd
One Threshold Does Not Fit All Languages: Language-Conditional Deferral for Reliable and Efficient Low-Resource Text Classification
Sep 29, 2026Bhanu Prakash Vangala, Vangala NavyaSelective Prediction Group-Conditional Conformal Prediction
University of Missouri · Amar Bio Tech Pvt Ltd
Discovering Translation-Worthy Languages with E-Values
Sep 6, 2026Wajdi Ben Saad, Safa MadiouniMultilingual Text Classification Text Classification
Carthago Labs Paris, France · IÉSEG Paris, France
ProBel: Propaganda Detection with Techniques, Spans, and Explanations
Aug 23, 2026Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori +2Text Classification Cross-Lingual Transfer
Qatar Computing Research Institute, Qatar · University of Padova, Italy
A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models
Aug 11, 2026Wajdi Ben Saad, Safa MadiouniZero-Shot Text Classification Multilingual Text Classification
Carthago Labs Paris, France · Université Paris Dauphine-PSL Paris, France
Predicting Multilingual Classification and Translation Performance of LLMs with Cross-Lingual Alignment $\unicode{x2013}$ Is English Enough?
Aug 4, 2026Adnan Al Ali, Kathy Hämmerl, Jindřich Libovický +1Multilingual Language Model Evaluation Cross-Lingual Representation Alignment
Charles University, Faculty of Mathematics and Physics, Czech Republic · Technical University of Munich, Germany · Munich Center for Machine Learning
Adversarial Creation and Detection of AI-Generated Social Bot Content
Jun 5, 2026Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini +2AI-Generated Text Detection Multilingual Text Classification
Universitat Pompeu Fabra, Barcelona, Spain · Observatory on Social Media, Indiana University, Bloomington, Indiana, USA · KTH Royal Institute of Technology, Stockholm, Sweden
Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection
Jun 2, 2026Pritam Kadasi, Anuj Tiwari, Mayank SinghPrompt Sensitivity Multilingual Text Classification
Lingo Research Group, Indian Institute of Technology Gandhinagar · Noida Institute of Engineering and Technology 2ML Collective
Uncovering Temporal Framing in the News
May 29, 2026Tarek Mahmoud, Veronika Solopova, Premtim Sahitaj +7Multilingual Text Classification
MBZUAI, UAE · Technische Universität Berlin, Germany · German Research Center for Artificial Intelligence (DFKI), Germany +1
100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts
May 9, 2026Rustem YeshpanovMultilingual Language Model Evaluation Sentiment Analysis
Independent Researcher / Astana, Kazakhstan
YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
May 7, 2026Fengze Guo, Yue ChangSocial Media Analysis Multilingual Text Classification
University of Tübingen
MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
Apr 23, 2026Maziar Kianimoghadam JouneghaniMultilingual Language Model Evaluation Language Model Ensembles
University of Turin
RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian
Apr 18, 2026Andrei-Marius Avram, Aureliu Valentin Antonie, Cosmin-Mircea Croitoru +2Domain-Adversarial Learning Unsupervised Domain Adaptation
National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
Comparison of Modern Multilingual Text Embedding Techniques for Hate Speech Detection Task
Apr 16, 2026Evaldas Vaiciukynas, Paulius Danenas, Linas Ablonskis +5Sentence Embeddings Text Embedding Evaluation
Department of Information Systems, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania · Centre of Information Systems Design Technologies, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
Multilingual Multi-Label Emotion Classification at Scale with Synthetic Data
Apr 14, 2026Vadim BorisovEmotion Recognition Multilingual Text Classification
SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
Apr 8, 2026Usman Naseem, Robert Geislinger, Juan Ren +31Social Media Analysis Multilingual Text Classification
Macquarie University · University of Hamburg · Zayed University +23
Multilingual Reference Need Assessment System for Wikipedia
Mar 17, 2026Aitolkyn Baigutanova, Francisco Navas, Pablo Aragon +5Multilingual Text Classification Text Classification
Wikimedia Foundation San Francisco, USA · Wikimedia Foundation Brooklyn, USA · Wikimedia Foundation Barcelona, Spain +3
To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noise
Mar 7, 2026Nouran Khallaf, Serge SharoffMultilingual Text Classification Uncertainty Calibration
Centre for Translation, Localisation and Interpreting Studies, School of Languages, Cultures and Societies, University of Leeds, UK
Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
Feb 19, 2026Deepak Uniyal, Md Abul Bashar, Richi NayakMultilingual Text Classification Cross-Lingual Transfer
Centre for Data Science, School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
GATech at AbjadGenEval Shared Task: Multilingual Embeddings for Arabic Machine-Generated Text Classification
Feb 17, 2026Ahmed Khaled KhamisMultilingual Text Classification AI-Generated Text Detection
Georgia Institute of Technology

Sep 29, 2026J/K move · Enter open · S save

How Many Labels Does a Language Need? Annotation Budgets and Cross-Lingual Pooling for African-Language Text Classification

Bhanu Prakash Vangala, Sowmya Guda, Navya Vangala

University of Missouri · Amar Bio Tech Pvt Ltd

Classification Low-Resource Language Processing Cross-Lingual Transfer Text Classification Zero-Shot Text Classification African Language Processing+1

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Every text classifier for an African language begins with a budgeting question: how many labelled examples are needed, and can labels from other African languages stand in for them? We answer both questions empirically for 28 language-task pairs, news topic classification in 16 languages (MasakhaNEWS) and tweet sentiment in 12 languages (AfriSenti), using a character n-gram linear model that trains in seconds on two CPU cores with no pretrained weights and no accelerator. Monolingual learning curves at budgets from 25 to several thousand labels show that topic classification reaches 90% of its full-data macro-F1 with about 400 labels in the median language, while sentiment is still improving at the full training size in 11 of 12 languages and needs thousands of labels. Pooling the full training data of the other languages in the benchmark is worth a great deal at small budgets and nothing at large ones: at 25 target labels it adds 0.20 macro-F1 on average for news (up to 0.43 for Lingala) and 0.08 for sentiment, the gain decays to zero by 800 labels, and at full size pooling hurts in 9 of 16 and 8 of 12 languages. Twenty-five target labels plus pooled data match what 100 to 400 monolingual labels achieve for most news languages. A complete zero-shot transfer matrix shows that transfer without any target labels recovers a median of only 13% (news) and 4% (sentiment) of the gap between a majority-class predictor and the in-language model, with the exceptions explained by shared script (Amharic and Tigrinya), shared lexicon (English and Nigerian Pidgin, the Arabic dialects), or a shared label prior rather than by language family. We release code that regenerates every number from the public benchmark files and translate the results into concrete annotation guidance for teams building African-language classifiers without GPUs.