Robustness and Adversarial Examples in Natural Language Processing (2021)

Abstract: Recent studies show that many NLP systems are sensitive and vulnerable to a small perturbation of inputs and do not generalize well across different datasets. This lack of robustness derails the use of NLP systems in real-world applications. This tutorial aims at bringing awareness of practical concerns about NLP robustness. It targets NLP researchers and practitioners who are interested in building reliable NLP systems. In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift. We will provide the audience with a holistic view of 1) how to use adversarial examples to examine the weakness of NLP models and facilitate debugging; 2) how to enhance the robustness of existing NLP models and defense against adversarial inputs; and 3) how the consideration of robustness affects the real-world NLP applications used in our daily lives. We will conclude the tutorial by outlining future research directions in this area.

## detail outline this tutorial presents a systematic overview of frontier approaches to generating adversarial examples to facilitate behavior testing and debugging of nlp systems. we will also review the studies revealing that nlp models make predictions based on spurious correlations learned in the data and discuss approaches to enhancing their robustness. we will motivate the discussion using various nlp tasks and will outline emerging research challenges on this topic at the end of the tutorial. the detailed contents covered in the tutorial are outlined below. ## reading list while the tutorial will include our own work @xcite @xcite @xcite @xcite @xcite @xcite , we anticipate that roughly 60% of the tutorial content will pull from work by other researchers in nlp and machine learning communities, including @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . a more comprehensive list of related papers will be provided before the tutorial. ## prerequisite knowledge our target audience is general nlp conference attendances; therefore, no specific knowledge is assumed of the audience except basic machine learning and nlp background: ## tutorial instructors our instructors consist of experts who have conducted research in different aspects related to the tutorial topic.