Recently, the adversarial attack and defense technology has made remarkable
achievements and has been widely applied in the computer vision field, promoting its rapid …

Having a clean dataset has been the foundational assumption of most natural language
processing (NLP) systems. However, properly written text is rarely found in real-world …

Stock price trend prediction has been a hot issue in the financial field, which has been paid
much attention by both academia and industry. It is a challenging task due to the non …

The development of accurate and scalable cross-modal image-text retrieval methods, where
queries from one modality (eg, text) can be matched to archive entries from another (eg …

The landscape of adversarial attacks against text classifiers continues to grow, with new
attacks developed every year and many of them available in standard toolkits, such as …

Recently, the problem of robustness of pre-trained language models (PrLMs) has received
increasing research interest. Latest studies on adversarial attacks achieve high attack …

Neural networks (NN) classification models for Natural Language Processing (NLP) are
vulnerable to the Universal Adversarial Triggers (UAT) attack that triggers a model to …

Deep neural networks have been proven to be vulnerable to adversarial examples and
various methods have been proposed to defend against adversarial attacks for natural …

NLP models excel on tasks with clean inputs, but are less accurate with noisy inputs. In
particular, character-level noise such as human-written typos and adversarially-engineered …

The recent increase in dataset size has brought about significant advances in natural
language understanding. These large datasets are usually collected through automation …