Natural Language Inference

Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Springer Applied Intelligence (Impact Factor: 5.019 in 2021)
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Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
IEEE Access (Impact Factor: 3.367 in 2020; 1st place in Engineering & Computer Science (general) at Google Scholar Metrics)
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Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training for Semi-Supervised Text Classification
Preprint version of the Springer Applied Intelligence paper โ€œMaking Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Trainingโ€
DOI Preprint
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training for Semi-Supervised Text Classification