Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing

Understand

Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores.

• These interpretations need to be robust for trustworthy NLP applications in high-stake areas like medicine or finance.
• Our paper demonstrates how interpretations can be manipulated by making simple word perturbations on an input text.
• Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations).