Introducing Rhetorical Parallelism Detection: A New Task with Datasets,
  Metrics, and Baselines
Stephen Bothwell Justin DeBenedetto Theresa Crnkovich Hildegund Müller David Chiang
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.