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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
Sep 24, 2026Toqeer Ehsan, Miriam Butt, Sarmad Hussain +2Urdu Syntactic Structure
Reliable Intelligence Team, VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland. · Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany. · Center for Language Engineering, Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, 54890 Lahore, Pakistan. +2
Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu
Sep 12, 2026Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt +1Urdu Low-Resource Languages
University of Konstanz, Germany · University of Massachusetts Amherst, USA · Malaysia School of Information Technology, Monash University Malaysia +1
Pak3H: Evaluating the Cost of Cultural Mismatch in LLM Alignment with a Human-Contextualized Urdu Benchmark
Aug 30, 2026Abdullah Hashmat, Usman Naseem, Agha Ali RazaUrdu Large Language Model Alignment
School of Computing, Macquarie University, Sydney, Australia · Lahore University of Management Sciences, Lahore, Pakistan
Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning
Jun 24, 2026Azher Ali, Ibtsam Haider, Raja Khurram Shahzad +2Mathematical Reasoning Urdu
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan · Department of Communication, Quality Management and Information Systems, Mid Sweden University, Ostersund, Sweden
Multilingual Hematology Visual Question Answering Dataset
Jun 24, 2026Hajra Malik, Hafiza Tooba Aftab, Abdul Rehman +2Multilingual Healthcare Q&A Urdu
Information Technology University, Lahore · King Edward Medical University, Lahore
ROMEVA: Geometry-Preserving Vocabulary Expansion for Roman Urdu Language Models
Jun 21, 2026Mahnoor Khan, Afsheen Asif, Milhan Afzal Khan +2Multilingual Language Models Subword Tokenization
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan · Department of Computer Science, University of Agriculture Faisalabad, Faisalabad, Pakistan
Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
Jun 17, 2026Ramza Basharat, Muhammad Usman AliHandwritten Text Recognition Urdu
Department of Computer Science, University of Gujrat, Pakistan · Department of Computer Science, University of Gujrat, Gujrat, Punjab, Pakistan
DunbaaBERT: From Sacrifice to Semantics
May 26, 2026Iffat Maab, Waleed Jamil, Raphael SchmittUrdu Multilingual Language Models
Research and Development Center for Large Language Models (LLMC), National Institute of Informatics, Tokyo · Independent Researcher, Edinburgh, United Kingdom · School of Computation, Information and Technology, Technical University of Munich, Germany +1
TajikNLP: An Open-Source Toolkit for Comprehensive Text Processing of Tajik (Cyrillic Script)
May 6, 2026Mullosharif K. Arabov, Karomatullo Habibullozoda, Nurali ShirinovUrdu Natural Language Processing
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia · Bokhtar State University named after Nosiri Khusrav Bokhtar, Tajikistan
Evaluating Large Language Models on Urdu Idioms
Oct 20, 2025Muhammad Farmal Khan, Mousumi AkterUrdu Idiom
Technical University Dortmund · Research Center Trustworthy Data Science and Security
Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu
Date pendingToneema Zubair, Muhammad Junaid Asif, Faisal Kamiran +2Hate Speech Detection Urdu
Department of Computer Sciences Information Technology University Lahore (54000), Pakistan · Artificial Intelligence Technology Centre National Centre for Physics Islamabad (44000), Pakistan
UrduMMLU: A Massive Multitask Benchmark for Urdu Language Understanding
Date pendingAhmer Tabassum, Sarfraz Ahmad, Hasan Iqbal +3Urdu Multilingual Benchmark
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection
Date pendingFawzia Zehra (Fuzzy), Kara-Isitt, Sonal Khosla +1Urdu Multilingual Safety
Brunel University of London, UK · Hasso Plattner Institute Potsdam, Germany
UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking
Date pendingSarfraz Ahmad, Hasan Iqbal, Momina Ahsan +6Urdu Multilingual Benchmark
MBZUAI

Sep 24, 2026, cs.CLJ/K move · Enter open · S save

Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language

Toqeer Ehsan, Miriam Butt, Sarmad Hussain, Hassan Alhuzali+1

Reliable Intelligence Team, VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland. · Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany. · Center for Language Engineering, Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, 54890 Lahore, Pakistan.+2

Urdu Syntactic Structure Multilingual Language Models Multi--Task Learning Morphology

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We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency and dependency parsing. This paper offers four major contributions: 1) the conversion of the CLE-UTB phrase structure treebank into a dependency treebank by developing language-specific head-word and phrase-to-dependency label mapping rules; 2) a novel sequence labeling scheme that transforms the parsing task into a unified representation; 3) the training of contextualized word representations on a large 220 million tokens Urdu corpus collected from the web; and 4) development of parsing framework using two learning paradigms, single-task and multi-task learning. Several post-processing rules are applied to improve the quality of the automatically converted dependency structure treebank. The proposed sequence labeling scheme enables the use of a shared architecture that learns the syntactic structures from both grammatical structures simultaneously and hence improves generalization. Experiments show that the multi-task learning setup significantly enhances parsing performance, achieving an F1 score of 91.39 for constituency parsing (an improvement of 3.29 points) and a labeled attachment score of 85.69 for dependency parsing (an improvement of 1.49 points). These results demonstrate that learning cross-task representations provides measurable benefits and advances the state of syntactic parsing for Urdu.

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