In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …

This article studies the multimodal named entity recognition (MNER) and multimodal relation
extraction (MRE), which are important for content analysis and various applications. The …

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies
concentrate on fully pre-trained LLMs to better understand and improve LLMs' …

The information bottleneck (IB) principle has been proven effective in various NLP
applications. The existing work, however, only used either generative or information …

Transformer-based models, such as BERT and GPT, have been widely adopted in natural
language processing (NLP) due to their exceptional performance. However, recent studies …

Building robust deep neural networks (DNNs) against adversarial attacks is an important but
challenging task. Previous defense approaches mainly focus on developing new model …

Stance Detection is the task of identifying the position of an author of a text towards an issue
or a target. Previous studies on Stance Detection indicate that the existing systems are non …

Several studies have exposed the vulnerability of Natural Language Processing (NLP)
models to adversarial attacks, which are inputs crafted by attackers to deceive NLP models …

Aspect-based sentiment analysis (ABSA) is a fine-grained text classification task, and the
cutting-edge ABSA models have achieved outstanding performance. Unfortunately, the …

The summarization of conversation, that is, discourse over discourse, elevates pragmatic
considerations as a pervasive limitation of both summarization and other applications of …