Research Experience

Violence Inciting Text Detection (VITD) in Bangla

2023 | Shared Task at the BLP Workshop, co-located with EMNLP

• Applied semi-supervised self-training techniques to address class imbalance and small dataset limitations, significantly improving model performance.
• Enhanced dataset diversity and quality through back-translation using the Googletrans API, which improved semantic variety while correcting linguistic inconsistencies.
• Implemented an ensemble approach combining multiple models with bagging and majority voting to reduce prediction variance and increase overall robustness.
• Publication: Team_Syrax at BLP-2023 Task 1: Data Augmentation and Ensemble Based Approach for Violence Inciting Text Detection in Bangla

BRAINTEASER: A Novel Task Defying Common Sense

2024 | Shared Task at SemEval 2024, co-located with NAACL

• Designed data augmentation pipelines to improve model robustness for complex commonsense reasoning tasks.
• Conducted a comparative analysis of advanced language models.
• Publication: Deja Vu at SemEval 2024 Task 9: A Comparative Study of Advanced Language Models for Commonsense Reasoning

Improving Answer Space Diversity in Visual Question Answering (VQA)

2022 | Undergraduate Thesis Project

• Conducted a comparative study of various state-of-the-art VQA methods, identifying and analyzing their core limitations, particularly in answer distribution.
• Addressed the “Answer Space Diversity” limitation in the VQA v2 dataset by augmenting training data with automatically generated, contextually relevant question-answer pairs.
• Demonstrated improved model performance on long-tail and less frequent answer categories.