awesome-contrastive-learning-for-nlp
github.com/malteos/awesome-contrastive-learning-for-nlp ↗A collection of papers about contrastive learning for natural language processing.
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2018
- An efficient framework for learning sentence representations
- GILE: A Generalized Input-Label Embedding for Text Classification
- Learning deep representations by mutual information estimation and maximization
- Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency
- Representation Learning with Contrastive Predictive Coding
2021
- A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
- COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
- Contrasting distinct structured views to learn sentence embeddings
- Learning Transferable Visual Models From Natural Language Supervision
2020
- A Survey on Contrastive Self-supervised Learning
- Big Self-Supervised Models are Strong Semi-Supervised Learners
- CERT: Contrastive Self-supervised Learning for Language Understanding
- CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
- Contrastive Distillation on Intermediate Representations for Language Model Compression
- Contrastive Self-Supervised Learning for Commonsense Reasoning
2019
- A Theoretical Analysis of Contrastive Unsupervised Representation Learning
- Contrastive Attention Mechanism for Abstractive Sentence Summarization
- The MuCoW Test Suite at WMT 2019: Automatically Harvested Multilingual Contrastive Word Sense Disambiguation Test Sets for Machine Translation
- Transferable Contrastive Network for Generalized Zero-Shot Learning
Showing a sample of 30 resources. View the full list on GitHub →