Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Prompting method is regarded as one of the crucial progress for few-shot nature language
processing. Recent research on prompting moves from discrete tokens based``hard …

Prototypical network is a key algorithm to solve few-shot problems. Previous prototypical
network based methods average sentence embeddings of the same class to obtain …

Few-shot emotion recognition in conversation (FSERC) aims to classify the emotion of
utterances in conversations with only a few labeled conversations. However, there is limited …

Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently
with only few examples, by leveraging prior knowledge from historical tasks. However, most …

Few-shot text classification (FSTC) strives to predict classes not involved in the training by
learning from a few labeled examples. Currently, most tasks construct meta-tasks in a …

Few-shot text classification aims to perform class prediction by learning from a few examples
on labels. Prototypical Network (ProtoNet) is often used to solve the few-shot problem …

Event detection in a city is crucial for the government to listen to the voice of the citizens, be
aware of the real occurrences in a city, and then make wiser policies. However, in reality …

Large pre-trained language models (PLMs) have made significant progress in encoding
world knowledge and spawned a new set of learning paradigms including zero-shot, few …

Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text
classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and …