Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

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 …

Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

Pre-trained language representation models (PLMs) cannot well capture factual knowledge
from text. In contrast, knowledge embedding (KE) methods can effectively represent the …

Neural language representation models such as BERT pre-trained on large-scale corpora
can well capture rich semantic patterns from plain text, and be fine-tuned to consistently …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

General purpose relation extractors, which can model arbitrary relations, are a core
aspiration in information extraction. Efforts have been made to build general purpose …

Recently, considerable literature has grown up around the theme of few-shot named entity
recognition (NER), but little published benchmark data specifically focused on the practical …