  Authorship style transfer involves altering text to match the style of a
target author whilst preserving the original meaning. Existing unsupervised
approaches like STRAP have largely focused on style transfer to target authors
with many examples of their writing style in books, speeches, or other
published works. This high-resource training data requirement (often greater
than 100,000 words) makes these approaches primarily useful for style transfer
to published authors, politicians, or other well-known figures and authorship
styles, while style transfer to non-famous authors has not been well-studied.
We introduce the \textit{low-resource authorship style transfer} task, a more
challenging class of authorship style transfer where only a limited amount of
text in the target author's style may exist. In our experiments, we
specifically choose source and target authors from Reddit and style transfer
their Reddit posts, limiting ourselves to just 16 posts (on average ~500 words)
of the target author's style. Style transfer accuracy is typically measured by
how often a classifier or human judge will classify an output as written by the
target author. Recent authorship representations models excel at authorship
identification even with just a few writing samples, making automatic
evaluation of this task possible for the first time through evaluation metrics
we propose. Our results establish an in-context learning technique we develop
as the strongest baseline, though we find current approaches do not yet achieve
mastery of this challenging task. We release our data and implementations to
encourage further investigation.
