Interpretable Adversarial Perturbation in Input Embedding Space for Text
This addresses the problem of interpretability in adversarial training for NLP practitioners, offering an incremental improvement over existing methods.
The paper tackles the loss of interpretability in adversarial training for NLP by restricting perturbations in the input embedding space to directions toward existing words, enabling reconstruction of adversarial texts as actual word replacements while maintaining or improving task performance.
Following great success in the image processing field, the idea of adversarial training has been applied to tasks in the natural language processing (NLP) field. One promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts. However, this approach abandons such interpretability as generating adversarial texts to significantly improve the performance of NLP tasks. This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space. As a result, we can straightforwardly reconstruct each input with perturbations to an actual text by considering the perturbations to be the replacement of words in the sentence while maintaining or even improving the task performance.