Few-Shot Text Classification
25 papers with code • 5 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Few-Shot Text Classification
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RAFT
Most implemented papers
Induction Networks for Few-Shot Text Classification
zhongyuchen/few-shot-text-classification
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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
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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).
Diverse Few-Shot Text Classification with Multiple Metrics
Gorov/DiverseFewShot_Amazon
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NAACL 2018
We study few-shot learning in natural language domains.
Few-shot Text Classification with Distributional Signatures
YujiaBao/Distributional-Signatures
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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
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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
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
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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
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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
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
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).
