Tags
Language Models Generation Data augmentation Language Model Semi-supervised learning Text classification Pretrained Language Model Denoising Auto-Encoder Out-of-domain Self-training Sent Augment SASE Controlled text generation Self-Supervised learning Generative model Language models Prompt engineering Masked language model PET Control Summarization CTRLsum Prompt Meta-tuning Semi-Supervised Learning Natural Language Understanding Industry Virtual Adversarial Training# Language Models
# Generation
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Blank Language Models
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Do Not Have Enough Data? Deep Learning to the Rescue!
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CTRL: A CONDITIONAL TRANSFORMER LANGUAGE MODEL FOR CONTROLLABLE GENERATION
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EDA : Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
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CoCon: A Self-Supervised Approach For Controlled Text Generation
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How Can We Know What Language Models Know?
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How Many Data Points is a Prompt Worth?
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CTRLsum: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION
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Meta-tuning Language Models to Answer Prompts Better
# Data augmentation
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Do Not Have Enough Data? Deep Learning to the Rescue!
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EDA : Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
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MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
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Data Augmentation using Pre-trained Transformer Models
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SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
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Self-training Improves Pre-training for Natural Language Understanding
# Language Model
# Semi-supervised learning
# Text classification
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MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
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Data Augmentation using Pre-trained Transformer Models
# Pretrained Language Model
# Denoising Auto-Encoder
# Out-of-domain
# Self-training
# Sent Augment
# SASE
# Controlled text generation
# Self-Supervised learning
# Generative model
# Language models
# Prompt engineering
# Masked language model
# PET
# Control
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CTRLsum: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION
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Meta-tuning Language Models to Answer Prompts Better