Papers by Nusrat Jahan Prottasha
BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting (2025.findings-naacl)
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Mohammad Jahid Ibna Basher, Md Kowsher, Md Saiful Islam, Rabindra Nath Nandi, Nusrat Jahan Prottasha, Mehadi Hasan Menon, Tareq Al Muntasir, Shammur Absar Chowdhury, Firoj Alam, Niloofar Yousefi, Ozlem Garibay
| Challenge: | Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. |
| Approach: | They propose to integrate Bangla into a multilingual TTS pipeline with modifications to account for the phonetic and linguistic characteristics of the language. |
| Outcome: | The proposed framework improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech compared to state-of-the-art systems. |
Does Self-Attention Need Separate Weights in Transformers? (2025.naacl-industry)
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| Challenge: | Experimental results show a 66.53% reduction in parameter size within the attention block and competitive accuracy improvements of 3.55% and 0.89% over symmetric and pairwise attention-based models, respectively. |
| Approach: | They propose a simplified approach where a single weight matrix is used for Keys, Queries, and Values instead of separate matrices for each. |
| Outcome: | The proposed approach outperforms the BERT baseline on GLUE tasks even outperforming the standard BERT model in handling noisy and out-of-domain data. |
Propulsion: Steering LLM with Tiny Fine-Tuning (2025.coling-main)
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| Challenge: | Propulsion is a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the model’s parameters. |
| Approach: | They propose a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the parameters. |
| Outcome: | The proposed method reduces parameter count from 355.3 million to 0.086 million while maintaining competitive performance across benchmarks. |
Predicting Through Generation: Why Generation Is Better for Prediction (2025.acl-long)
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Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat, Chun-Nam Yu, Mojtaba Soltanalian, Ivan Garibay, Ozlem Garibay, Chen Chen, Niloofar Yousefi
| Challenge: | Large Language Models (LLMs) are increasingly used for predictive tasks such as classification and regression. |
| Approach: | They propose a framework that generates output tokens from mas-sive text corpora and a task adapter to ensure consistency between token generation and final prediction. |
| Outcome: | The proposed framework outperforms baseline models on classification and regression benchmarks and the proposed framework consistently outperformed standard baseline models. |