Papers by Ubaid Azam
Detecting Cybercrimes in Accordance with Pakistani Law: Dataset and Evaluation Using PLMs (2024.lrec-main)
Copied to clipboard
| Challenge: | Roman Urdu is a widely used language in Pakistan but lacks sufficient resources and tools for text-based cybercrime detection. |
| Approach: | They propose to use a benchmark dataset for text-based cybercrime detection in Roman Urdu to improve the performance of pre-trained language models. |
| Outcome: | The proposed model achieves the highest performance on all metrics. |
Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in natural language processing have demonstrated the efficacy of pre-trained language models for various downstream tasks. |
| Approach: | They compare prompt-based fine-tuning with standard fine-uning for text classification in Urdu and Roman Urdu languages. |
| Outcome: | The proposed approach improves up to 13% in accuracy in low-resource languages with limited labeled examples over standard fine-tuning approaches. |
Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu (2022.lrec-1)
Copied to clipboard
| Challenge: | a number of social media platforms are generating hateful content, a new study finds . augmentation techniques are needed to improve the performance of the models . |
| Approach: | They evaluate different data augmentation techniques for the improvement of hate speech detection in Roman Urdu. |
| Outcome: | The proposed techniques improve hate speech detection in Roman Urdu on two datasets. |
Uncertainty Modelling in Under-Represented Languages with Bayesian Deep Gaussian Processes (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for NLP modeling underrepresented languages are limited due to lack of training data and language complexities. |
| Approach: | They propose a new method that integrates prior knowledge and leverages kernel functions to quantify uncertainty in under-represented languages. |
| Outcome: | The proposed method improves prediction accuracy and measurement of uncertainty in under-represented languages. |