  In many applications of machine learning, certain categories of examples may
be underrepresented in the training data, causing systems to underperform on
such "few-shot" cases at test time. A common remedy is to perform data
augmentation, such as by duplicating underrepresented examples, or
heuristically synthesizing new examples. But these remedies often fail to cover
the full diversity and complexity of real examples.
  We propose a data augmentation approach that performs neural Example
Extrapolation (Ex2). Given a handful of exemplars sampled from some
distribution, Ex2 synthesizes new examples that also belong to the same
distribution. The Ex2 model is learned by simulating the example generation
procedure on data-rich slices of the data, and it is applied to
underrepresented, few-shot slices.
  We apply Ex2 to a range of language understanding tasks and significantly
improve over state-of-the-art methods on multiple few-shot learning benchmarks,
including for relation extraction (FewRel) and intent classification + slot
filling (SNIPS).
