Practical applications of Natural Language Processing (NLP) have gotten significantly cheaper, faster, and easier due to the transfer learning capabilities enabled by pre-trained language models. Transfer learning enables engineers to pre-train an NLP model on one large dataset and then quickly fine-tune the model to adapt to other NLP tasks.

This new approach enables NLP models to learn both lower-level and higher-level features of language, leading to much better model performance for virtually all standard NLP tasks and a new standard for industry best practices.

To help you quickly understand the significance of this technical achievement and how it accelerates your own work in NLP, we’ve summarized the key lessons you should know in easy-to-read bullet-point format. We’ve also included summaries of the 3 most important research papers in the space that you need to be aware of. Read More

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