Developed an academic machine learning replication paper that improved upon and scrutinized a state-of-the-art model’s performance on the Stanford Natural Language Inference (SNLI) dataset.
Conducted comprehensive analysis on over 570,000 SNLI instances, identifying and addressing challenging cases that caused model errors, demonstrating strong analytical and problem-solving skills.
Implemented advanced NLP techniques to enhance model robustness:
Fine-tuning
Adversarial training
Ensemble methods
Successfully outperformed the results reported in the original paper being replicated, showcasing ability to innovate and improve upon existing research.
