# Few-Shot Text Classification

25 papers with code • 5 benchmarks • 1 datasets

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# Induction Networks for Few-Shot Text Classification

Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.

5

# Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

21 Jan 2020

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e. g., Radford et al., 2019).

5

# Diverse Few-Shot Text Classification with Multiple Metrics

We study few-shot learning in natural language domains.

2

# Few-shot Text Classification with Distributional Signatures

In this paper, we explore meta-learning for few-shot text classification.

2

# Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification.

2

# Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop

5 Apr 2018

Our work aims to make it possible to classify an entire corpus of unlabeled documents using a human-in-the-loop approach, where the content owner manually classifies just one or two documents per category and the rest can be automatically classified.

1

# Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels.

1

# A Neural Few-Shot Text Classification Reality Check

Additionally, some models used in Computer Vision are yet to be tested in NLP applications.

1

# Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.

1

# Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with {}task descriptions{''} in natural language (e. g., Radford et al., 2019).

1