We introduce an adversarial method for producing high-recall explanations of neural text
classifier decisions. Building on an existing architecture for extractive explanations via hard …

Deep learning (DL) models have significantly improved the performance of text classification
and text regression tasks. However, DL models are often strikingly vulnerable to adversarial …

Targeted phishing emails are on the rise and facilitate the theft of billions of dollars from
organizations a year. While malicious signals from attached files or malicious URLs in …

The growing use of social media has led to the development of several Machine Learning
(ML) and Natural Language Processing (NLP) tools to process the unprecedented amount …

Social networks have become an indispensable part of our lives, with billions of people
producing ever-increasing amounts of text. At such scales, content policies and their …

With the preponderance of harassment and abuse, social media platforms and online
discussion platforms seek to curb toxic comments. Google's Perspective aims to help …

Today text classification models have been widely used. However, these classifiers are
found to be easily fooled by adversarial examples. Fortunately, standard attacking methods …

Recently, generating adversarial examples has become an important means of measuring
robustness of a deep learning model. Adversarial examples help us identify the …

Adversarial attacks against machine learning models have threatened various real-world
applications such as spam filtering and sentiment analysis. In this paper, we propose a …

Deep neural networks have achieved remarkable results across many language processing
tasks, however they have been shown to be susceptible to overfitting and highly sensitive to …