Improving the Reliability of Deep Neural Networks in NLP: A Review

Abstract

Deep learning models have achieved great success in solving a variety of natural language processing (NLP) problems. An ever-growing body of research, however, illustrates the vulnerability of deep neural networks (DNNs) to adversarial examples — inputs modified by introducing small perturbations to deliberately fool a target model into outputting incorrect results. The vulnerability to adversarial examples has become one of the main hurdles precluding neural network deployment into safety-critical environments. This paper discusses the contemporary usage of adversarial examples to foil DNNs and presents a comprehensive review of their use to improve the robustness of DNNs in NLP applications. In this paper, we summarize recent approaches for generating adversarial texts and propose a taxonomy to categorize them. We further review various types of defensive strategies against adversarial examples, explore their main challenges, and highlight some future research directions.