The study examines Urdu light verbs, which add schematic event meaning while staying lexically linked to their main verbs. Using contextual embeddings from UrduBERT, DunbaaBERT, and multilingual BERT on 1,126 sentences, the authors find significant representational separation between main and light uses across all verb–model pairs, yet main and light uses of the same lemma remain closer than mismatched pairs. In a seven‑way prediction task limited to light uses, UrduBERT achieves 0.866 accuracy and 0.852 macro‑F1, and maintains 0.782 accuracy when tested on unseen preceding forms, demonstrating generalization beyond local verb combinations.
By Farah Adeeba, Miriam Butt
The paper introduces a new benchmark for Urdu‑to‑English idiomatic translation, featuring 4,000 manually verified sentence pairs in both native Perso‑Arabic script and Romanized Urdu. It evaluates multiple tasks—translation, paraphrasing, idiom span detection, and back‑translation—using various prompting strategies, and finds that state‑of‑the‑art large language models outperform traditional neural machine translation systems, especially in preserving figurative meaning. The study also highlights challenges posed by the lack of standardized orthography in Romanized Urdu, which affects consistency and idiom span detection.
By Muhammad Farmal Khan, Mousumi Akter
The paper evaluates large language models for hate‑speech detection in Roman Urdu, a low‑resource language with informal spelling variations. Using the Parameter‑Efficient Fine‑Tuning technique Low‑Rank Adaptation (LoRA), the authors fine‑tune models such as Mistral, LLaMA, Falcon, and multilingual BERT on the 72,000‑comment PURUTT dataset. While zero‑shot performance yields an F1 of 0.56, fine‑tuning a small fraction of parameters boosts F1 scores above 0.93, demonstrating that PEFT offers both high accuracy and computational efficiency for low‑resource language tasks.
By Toneema Zubair, Muhammad Junaid Asif, Faisal Kamiran, Hafiz Hassan Saeed, Rana Fayyaz Ahmad
arXiv:2606. 07167v1 Announce Type: cross Abstract: Meaningful multilingual evaluation must test models in the target language and educational context.
By Ahmer Tabassum, Sarfraz Ahmad, Hasan Iqbal, Owais Aijaz, Momina Ahsan, Preslav Nakov
arXiv:2608.22922v1 Announce Type: new
Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...
By Thisen Ekanayake, Nisansa de Silva
arXiv:2608.21408v1 Announce Type: new
Abstract: Due to the widespread accessibility of the internet and social media, toxic and hateful con-tent has grown exponentially, causing significant distress...
By Toneema Zubair