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Meta-Learning for Few-Shot Text Classification by Tough Hou is a document available to read on EtoBox.

What is Meta-Learning for Few-Shot Text Classification about?

This paper presents a novel approach called Meta-pretraining Then Meta-learning (MTM) to enhance few-shot text classification in low-resource NLP scenarios by leveraging unsupervised language models. The proposed method effectively disentangles task-agnostic and task-specific representations, achieving state-of-the-art performance on sentiment classification tasks. Experimental results indicate that MTM outperforms existing meta-learning models, suggesting its potential applicability to various NLP tasks.

Author

Tough Hou

Language

EN