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

arXiv: https://arxiv.org/abs/2312.00100 · HTML: https://mlsanity.com/paper/2312.00100/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 91 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Transformers without Tears: Improving the Normalization of Self-Attention](https://mlsanity.com/paper/1910.05895/) (2019)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [On the Practical Computational Power of Finite Precision RNNs for Language Recognition](https://mlsanity.com/paper/1805.04908/) (2018)
- [The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://mlsanity.com/paper/1804.09849/) (2018)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging](https://mlsanity.com/paper/1707.09861/) (2017)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [LSTM Networks Can Perform Dynamic Counting](https://mlsanity.com/paper/1906.03648/) (2019)
- [Latin BERT: A Contextual Language Model for Classical Philology](https://mlsanity.com/paper/2009.10053/) (2020)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- [On Evaluating the Generalization of LSTM Models in Formal Languages](https://mlsanity.com/paper/1811.01001/) (2018)
- [Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms](https://mlsanity.com/paper/2105.03280/) (2021)
- [Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection](https://mlsanity.com/paper/1904.04388/) (2019)
- [SGG: Spinbot, Grammarly and GloVe based Fake News Detection](https://mlsanity.com/paper/2008.06854/) (2020)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- ["You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates](https://mlsanity.com/paper/1802.08690/) (2018)
- [Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining](https://mlsanity.com/paper/2107.07113/) (2021)

## Built on by (most cited first, 7 of 7)

- [SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches](https://mlsanity.com/paper/2503.03703/) (2025)
- [Quantitative Intertextuality from the Digital Humanities Perspective: A Survey](https://mlsanity.com/paper/2510.27045/) (2025)
- [Computational Discovery of Chiasmus in Ancient Religious Text](https://mlsanity.com/paper/2501.10739/) (2025)
- [Modeling Narrative Structure in Latin Epic Poetry with Automatically Generated Story Grammars](https://mlsanity.com/paper/2502.12276/) (2025)
- [Rewrite the News: Tracing Editorial Reuse Across News Agencies](https://mlsanity.com/paper/2603.29937/) (2026)
- [MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection](https://mlsanity.com/paper/2606.19638/) (2026)
- [Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it](https://mlsanity.com/paper/2607.21498/) (2026)

## Abstract (arXiv)

Rhetoric, both spoken and written, involves not only content but also style. One common stylistic tool is textitparallelism: the juxtaposition of phrases which have the same sequence of linguistic (textite.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 textitrhetorical 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 F1 scores of 0.40 and 0.43 on our Latin and Chinese datasets, respectively.