This repository serves as a curated list of academic papers focused on Contrastive Learning (CL) within the Natural Language Processing (NLP) domain. It aims to provide researchers and practitioners with a comprehensive overview of CL's foundational concepts, methodologies, and diverse applications in NLP tasks, facilitating the adoption and advancement of this representation learning technique.

How It Works

The repository categorizes papers based on their contribution to contrastive learning in NLP, covering foundational principles like loss functions and sampling strategies, to specific applications such as text classification, sentence embeddings, and information extraction. It also includes papers on interpretability, commonsense reasoning, and vision-language tasks, showcasing the breadth of CL's impact.

Highlighted Details

• Extensive coverage of contrastive learning objectives, sampling strategies, and analysis.
• Detailed sections on applications across various NLP tasks including text classification, sentence embeddings, information extraction, machine translation, question answering, summarization, and text generation.
• Inclusion of papers on data-efficient learning, contrastive pretraining, interpretability, commonsense reasoning, and vision-language integration.
• Links to official websites, slides, videos, and code repositories are provided for many entries.

Maintenance & Community

This is a curated list of papers, not an active software project. The primary contribution is the compilation and organization of research.

Licensing & Compatibility

This repository contains links to external academic papers and does not have its own software license. Compatibility is dependent on the licenses of the linked resources.

Limitations & Caveats

The repository is a static compilation of research papers and does not provide any code, implementations, or benchmarks for contrastive learning methods. Its utility is limited to literature review and discovery.