Benchmarks
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Greatest papers with code
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
• timoschick/pet
• 
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).
CLASSIFICATION FEW-SHOT TEXT CLASSIFICATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE TEXT CLASSIFICATION
Few-shot Text Classification with Distributional Signatures
ICLR 2020
• YujiaBao/Distributional-Signatures
•
In this paper, we explore meta-learning for few-shot text classification.
CLASSIFICATION FEW-SHOT TEXT CLASSIFICATION META-LEARNING RELATION CLASSIFICATION TEXT CLASSIFICATION
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification
• zxlzr/FewShotNLP
•
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes.
Ranked #1 on
Multi-Domain Sentiment Classification
on ARSC
CLASSIFICATION FEW-SHOT LEARNING FEW-SHOT TEXT CLASSIFICATION LANGUAGE MODELLING SENTIMENT ANALYSIS TEXT CLASSIFICATION
Diverse Few-Shot Text Classification with Multiple Metrics
NAACL 2018
• Gorov/DiverseFewShot_Amazon
•
We study few-shot learning in natural language domains.
CLASSIFICATION FEW-SHOT LEARNING FEW-SHOT TEXT CLASSIFICATION INTENT CLASSIFICATION METRIC LEARNING SENTIMENT ANALYSIS 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.
CLASSIFICATION FEW-SHOT LEARNING FEW-SHOT TEXT CLASSIFICATION TEXT CLASSIFICATION WORD EMBEDDINGS
Induction Networks for Few-Shot Text Classification
IJCNLP 2019
• laohur/LearnToCompareText
•
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
Ranked #1 on
Few-Shot Text Classification
on ODIC 5-way (10-shot)
CLASSIFICATION FEW-SHOT TEXT CLASSIFICATION INTENT CLASSIFICATION META-LEARNING SENTIMENT ANALYSIS TEXT CLASSIFICATION
A Neural Few-Shot Text Classification Reality Check
Additionally, some models used in Computer Vision are yet to be tested in NLP applications.
CLASSIFICATION FEW-SHOT TEXT CLASSIFICATION INTENT DETECTION TEXT CLASSIFICATION WORD EMBEDDINGS
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.
CLASSIFICATION CURRICULUM LEARNING DATA AUGMENTATION FEW-SHOT TEXT CLASSIFICATION TEXT CLASSIFICATION