Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

In this paper, a semantic communication framework is proposed for textual data
transmission. In the studied model, a base station (BS) extracts the semantic information …

As large language models (LLMs) have demonstrated their powerful capabilities in plenty of
domains and tasks, including context understanding, code generation, language generation …

Proposal of large-scale datasets has facilitated research on deep neural models for news
summarization. Deep learning can also be potentially useful for spoken dialogue …

We propose a novel hierarchical graph-based text classification framework that leverages
the power of contextual node embedding and BERT-based dynamic fusion to capture the …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Graph neural networks (GNNs) have become a popular approach to integrating structural
inductive biases into NLP models. However, there has been little work on interpreting them …