  Rhetoric, both spoken and written, involves not only content but also style.
One common stylistic tool is $\textit{parallelism}$: the juxtaposition of
phrases which have the same sequence of linguistic ($\textit{e.g.}$,
phonological, syntactic, semantic) features. Despite the ubiquity of
parallelism, the field of natural language processing has seldom investigated
it, missing a chance to better understand the nature of the structure, meaning,
and intent that humans convey. To address this, we introduce the task of
$\textit{rhetorical parallelism detection}$. We construct a formal definition
of it; we provide one new Latin dataset and one adapted Chinese dataset for it;
we establish a family of metrics to evaluate performance on it; and, lastly, we
create baseline systems and novel sequence labeling schemes to capture it. On
our strictest metric, we attain $F_{1}$ scores of $0.40$ and $0.43$ on our
Latin and Chinese datasets, respectively.
