Natural language processing models require a ton of text data and computing
time - resources that aren't available to most. But now you can use powerful
pre-trained NLP models from Google, AllenNLP, and others who have done the
heavy lifting for you, accessible through straightforward python libraries.
Transfer learning allows you to tailor these massive models to your specific
2018 ushered in a new era for NLP based on a series of breakthroughs in the
use of transfer learning. Traditionally only word vectors such as word2vec,
which provide rich representations of _words_, have been reused across
different NLP tasks. However, word vectors have no notion of the context in
which a word occurs, which is severely limiting. _Context-aware_ transfer
learning is a powerful new technique that has already made a great impact.
Within a year, transfer learning has changed the state-of-the-art in nearly
every category. It is being used to generate extremely realistic text, and
some work has even been kept secret for fear of its potential for misuse. It
has made improvements in sentiment analysis, document topic classification,
and named entity extraction.
But being so new, it’s not well understood how these techniques work, what
types of problems benefit most, what hazards exist, or what tools to use. In
this talk we’ll cover new techniques in transfer learning, their impacts,
and present a framework for how to apply them. We provide recommendations
for choosing candidate problems, how to conduct experiments, how to
understand and interpret these models, and the available python tooling.