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Greatest papers with code

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

21 Jan 2020timoschick/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 Pre-Trained Word Embeddings and a Human in the Loop

5 Apr 2018katbailey/few-shot-text-classification

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

A Neural Few-Shot Text Classification Reality Check

28 Jan 2021tdopierre/FewShotText

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

12 Mar 2021jasonwei20/triplet-loss

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