Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

Sentiment and emotion, which correspond to long-term and short-lived human feelings, are
closely linked to each other, leading to the fact that sentiment analysis and emotion …

Abstract Aspect Sentiment Triplet Extraction (ASTE) aims to extract the aspect terms along
with the corresponding opinion terms and the expressed sentiments in the review, which is …

Aspect-based sentiment analysis refers to the task of determining the sentiment polarity
associated with particular aspects mentioned in a sentence or document. Previous studies …

The use of machine learning (ML) models to assess and score textual data has become
increasingly pervasive in an array of contexts including natural language processing …

Emotion recognition in conversations (ERC) needs to detect the emotion of each utterance
in conversations. However, it is difficult for machines to recognize the emotion of utterances …

Multimodal entity-based sentiment analysis (MEBSA) is an emerging task in sentiment
analysis that aims to identify three key elements (entity, entity category, and sentiment …

Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon
where the intended meaning is often opposite of the literal expression. Current sarcasm …

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …