Few-Shot Text Classification

25 papers with code • 5 benchmarks • 1 datasets

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Datasets


Most implemented papers

Induction Networks for Few-Shot Text Classification

zhongyuchen/few-shot-text-classification IJCNLP 2019

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

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

timoschick/pet 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).

Few-shot Text Classification with Distributional Signatures

YujiaBao/Distributional-Signatures ICLR 2020

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

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

thunlp/knowledgeableprompttuning ACL 2022

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

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

katbailey/few-shot-text-classification 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.

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

timoschick/pet COLING 2020

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.

A Neural Few-Shot Text Classification Reality Check

tdopierre/FewShotText EACL 2021

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

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

jasonwei20/triplet-loss NAACL 2021

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.

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

timoschick/pet EACL 2021

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).