2021年ACL定会 few-shot learning相关paper
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如下所示：
Multi-Label Few-Shot Learning for Aspect Category Detection
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
Few-Shot Question Answering by Pretraining Span Selection
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Distinct Label Representations for Few-Shot Text Classification
On Training Instance Selection for Few-Shot Neural Text Generation
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification
Reordering Examples Helps during Priming-based Few-Shot Learning
Frustratingly Simple Few-Shot Slot Tagging
UserAdapter: Few-Shot User Learning in Sentiment Analysis
Few-Shot Upsampling for Protest Size Detection