The Future of (Transfer Learning in) Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.
natural-language-processing transfer-learning tutorial
Resource links
Top collections
Details
Objectives & Highlights

A walk through interesting papers and research directions in late 2019/early-2020 on: - model size and computational efficiency, - out-of-domain generalization and model evaluation, - fine-tuning and sample efficiency, - common sense and inductive biases.

Don't forget to tag @thomwolf in your comment.

Authors community post
Science Lead @ Huggingface Inc.
Share this project
Similar projects
The State of Transfer Learning in NLP
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. It highlights key insights and takeaways and provides updates based on recent ...
Transfer Learning In NLP
A brief history of Transfer Learning In NLP
DialoGPT: Toward Human-Quality Conversational Response Generation
Large-scale pre-training for dialogue.
NLP Model Selection
NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.