Few-Shot Text Classification
37 papers with code • 8 benchmarks • 1 datasets
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Most implemented papers
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).
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.
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification.
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.
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.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
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.
A Neural Few-Shot Text Classification Reality Check
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.