The Future of Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions. Slides: https://tinyurl.com/FutureOfNLP
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.
by Thomas Wolf (Science lead at HuggingFace)
HuggingFace on Twitter: https://twitter.com/huggingface
Thomas Wolf on Twitter: https://twitter.com/Thom_Wolf
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions. Slides: https://tinyurl.com/FutureOfNLP
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.
by Thomas Wolf (Science lead at HuggingFace)
HuggingFace on Twitter: https://twitter.com/huggingface
Thomas Wolf on Twitter: https://twitter.com/Thom_Wolf
