Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines

• URL: http://arxiv.org/abs/2312.00100v1
• Date: Thu, 30 Nov 2023 15:24:57 GMT
• Title: Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines
• Authors: Stephen Bothwell, Justin DeBenedetto, Theresa Crnkovich, Hildegund M\"uller, David Chiang
• Abstract summary: parallelism$ is the juxtaposition of phrases which have the same sequence of linguistic features. Despite the ubiquity of parallelism, the field of natural language processing has seldom investigated it. 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.
• Score: 8.405938712823565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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