Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

Cross-domain sentiment analysis has achieved promising results with the help of pre-
trained language models. As GPT-3 appears, prompt tuning has been widely explored to …

Cross-domain sentiment classification aims to address the lack of massive amounts of
labeled data. It demands to predict sentiment polarity on a target domain utilizing a classifier …

Towards the challenging problem of semi-supervised node classification, there have been
extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

Humans are increasingly integrated with devices that enable the collection of vast
unstructured opinionated data. Accurately analysing subjective information from this data is …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …