In this talk, David Talby summarized four key trends that are shaping the NLP industry and open source ecosystem today:
Models need better zookeepers: As the number of publicly available NLP models explode, it becomes harder to find the one you should actually use for your next project. New model hubs are adding are adding better search, discovery, and curation.
Multi-lingual models: Thanks to recent advances in transfer learning and the public availability of multi-lingual embeddings, open sources libraries that support dozens of languages out of the box are becoming the norm for the first time.
State-of-the-art models are one-liners: Running many of the most accurate & most complex deep learning models in history has been reduced to a single line of Python code.
“Pre-train and tune” gets automated: While you still have to train your own models to understand domain specific text, “Auto-NLP” is coming fast behind the “Auto-ML” trend to make this a code-free process.