19 citations · 20 across the 6 of their papers we have counts for
7 papers
Exploring Cross-Lingual Knowledge Transfer via Transliteration-Based MLM Fine-Tuning for Critically Low-resource Chakma Language
Adity Khisa, Nusrat Jahan Lia, Tasnim Mahfuz Nafis +4
As an Indo-Aryan language with limited available data, Chakma remains largely underrepresented in language models. In this work, we introduce a novel corpus of contextually coheren…
Stemming -- The Evolution and Current State with a Focus on Bangla
Abhijit Paul, Mashiat Amin Farin, Sharif Md. Abdullah +3
Bangla, the seventh most widely spoken language worldwide with 300 million native speakers, faces digital under-representation due to limited resources and lack of annotated datase…
Where Journalism Silenced Voices: Exploring Discrimination in the Representation of Indigenous Communities in Bangladesh
Abhijit Paul, Adity Khisa, Zarif Masud +3
In this paper, we examine the intersections of indigeneity and media representation in shaping perceptions of indigenous communities in Bangladesh. Using a mixed-methods approach,…
The Rise of Small Language Models in Healthcare: A Comprehensive Survey
Muskan Garg, Shaina Raza, Shebuti Rayana +2
Despite substantial progress in healthcare applications driven by large language models (LLMs), growing concerns around data privacy, and limited resources; the small language mode…
Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation
Sharif Mohammad Abdullah, Abhijit Paul, Shubhashis Roy Dipta +3
Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing population of at least 3 million in Bangl…
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
Shebuti Rayana, Wen Zhong, Leman Akoglu
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this wor…
Less is More: Building Selective Anomaly Ensembles
Shebuti Rayana, Leman Akoglu
Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a n…